Geographical patterns in prediction errors of species distribution models

Research output: Journal contributionsJournal articlesResearch

Standard

Geographical patterns in prediction errors of species distribution models. / Hanspach, Jan; Kühn, Ingolf; Schweiger, Oliver et al.
In: Global Ecology and Biogeography, Vol. 20, No. 5, 01.09.2011, p. 779-788.

Research output: Journal contributionsJournal articlesResearch

Harvard

APA

Vancouver

Hanspach J, Kühn I, Schweiger O, Pompe S, Klotz S. Geographical patterns in prediction errors of species distribution models. Global Ecology and Biogeography. 2011 Sept 1;20(5):779-788. doi: 10.1111/j.1466-8238.2011.00649.x

Bibtex

@article{0c7506266f574ae2a4d3e7dd908e27c9,
title = "Geographical patterns in prediction errors of species distribution models",
abstract = "Aim To describe and explain geographical patterns of false absence and false presence prediction errors that occur when describing current plant species ranges with species distribution models. Location Europe. Methods We calibrated species distribution models (generalized linear models) using a set of climatic variables and gridded distribution data for 1065 vascular plant species from the Atlas Florae Europaeae. We used randomly selected subsets for each species with a constant prevalence of 0.5, modelled the distribution 1000 times, calculated weighted averages of the model parameters and used these to predict the current distribution in Europe. Using a threshold of 0.5, we derived presence/absence maps. Comparing observed and modelled species distribution, we calculated the false absence rates, i.e. species wrongly modelled as absent, and the false presence rates, i.e. species wrongly modelled as present, on a 50 × 50km grid. Subsequently, we related both error rates to species range properties, land use and topographic variability within grid cells by means of simultaneous autoregressive models to correct for spatial autocorrelation. Results Grid-cell-specific error rates were not evenly distributed across Europe. The mean false absence rate was 0.16 ± 0.12 (standard deviation) and the mean false presence rate was 0.22 ± 0.13. False absence rates were highest in central Spain, the Alps and parts of south-eastern Europe, while false presence rates were highest in northern Spain, France, Italy and south-eastern Europe. False absence rates were high when range edges of species accumulated within a grid cell and when the intensity of human land use was high. False presence rates were positively associated with relative occurrence area and accumulation of range edges. Main conclusions Predictions for various species are not only accompanied by species-specific but also by grid-cell-specific errors. The latter are associated with characteristics of the grid cells but also with range characteristics of occurring species. Uncertainties of predictive species distribution models are not equally distributed in space, and we would recommend accompanying maps of predicted distributions with a graphical representation of predictive performance.",
keywords = "Environmental planning, Commission, Europe, False absence rate, False presence rate, Omission, Species distribution models, Validation",
author = "Jan Hanspach and Ingolf K{\"u}hn and Oliver Schweiger and Sven Pompe and Stefan Klotz",
note = "Copyright 2011 Elsevier B.V., All rights reserved.",
year = "2011",
month = sep,
day = "1",
doi = "10.1111/j.1466-8238.2011.00649.x",
language = "English",
volume = "20",
pages = "779--788",
journal = "Global Ecology and Biogeography",
issn = "1466-822X",
publisher = "Wiley-Blackwell Publishing Ltd.",
number = "5",

}

RIS

TY - JOUR

T1 - Geographical patterns in prediction errors of species distribution models

AU - Hanspach, Jan

AU - Kühn, Ingolf

AU - Schweiger, Oliver

AU - Pompe, Sven

AU - Klotz, Stefan

N1 - Copyright 2011 Elsevier B.V., All rights reserved.

PY - 2011/9/1

Y1 - 2011/9/1

N2 - Aim To describe and explain geographical patterns of false absence and false presence prediction errors that occur when describing current plant species ranges with species distribution models. Location Europe. Methods We calibrated species distribution models (generalized linear models) using a set of climatic variables and gridded distribution data for 1065 vascular plant species from the Atlas Florae Europaeae. We used randomly selected subsets for each species with a constant prevalence of 0.5, modelled the distribution 1000 times, calculated weighted averages of the model parameters and used these to predict the current distribution in Europe. Using a threshold of 0.5, we derived presence/absence maps. Comparing observed and modelled species distribution, we calculated the false absence rates, i.e. species wrongly modelled as absent, and the false presence rates, i.e. species wrongly modelled as present, on a 50 × 50km grid. Subsequently, we related both error rates to species range properties, land use and topographic variability within grid cells by means of simultaneous autoregressive models to correct for spatial autocorrelation. Results Grid-cell-specific error rates were not evenly distributed across Europe. The mean false absence rate was 0.16 ± 0.12 (standard deviation) and the mean false presence rate was 0.22 ± 0.13. False absence rates were highest in central Spain, the Alps and parts of south-eastern Europe, while false presence rates were highest in northern Spain, France, Italy and south-eastern Europe. False absence rates were high when range edges of species accumulated within a grid cell and when the intensity of human land use was high. False presence rates were positively associated with relative occurrence area and accumulation of range edges. Main conclusions Predictions for various species are not only accompanied by species-specific but also by grid-cell-specific errors. The latter are associated with characteristics of the grid cells but also with range characteristics of occurring species. Uncertainties of predictive species distribution models are not equally distributed in space, and we would recommend accompanying maps of predicted distributions with a graphical representation of predictive performance.

AB - Aim To describe and explain geographical patterns of false absence and false presence prediction errors that occur when describing current plant species ranges with species distribution models. Location Europe. Methods We calibrated species distribution models (generalized linear models) using a set of climatic variables and gridded distribution data for 1065 vascular plant species from the Atlas Florae Europaeae. We used randomly selected subsets for each species with a constant prevalence of 0.5, modelled the distribution 1000 times, calculated weighted averages of the model parameters and used these to predict the current distribution in Europe. Using a threshold of 0.5, we derived presence/absence maps. Comparing observed and modelled species distribution, we calculated the false absence rates, i.e. species wrongly modelled as absent, and the false presence rates, i.e. species wrongly modelled as present, on a 50 × 50km grid. Subsequently, we related both error rates to species range properties, land use and topographic variability within grid cells by means of simultaneous autoregressive models to correct for spatial autocorrelation. Results Grid-cell-specific error rates were not evenly distributed across Europe. The mean false absence rate was 0.16 ± 0.12 (standard deviation) and the mean false presence rate was 0.22 ± 0.13. False absence rates were highest in central Spain, the Alps and parts of south-eastern Europe, while false presence rates were highest in northern Spain, France, Italy and south-eastern Europe. False absence rates were high when range edges of species accumulated within a grid cell and when the intensity of human land use was high. False presence rates were positively associated with relative occurrence area and accumulation of range edges. Main conclusions Predictions for various species are not only accompanied by species-specific but also by grid-cell-specific errors. The latter are associated with characteristics of the grid cells but also with range characteristics of occurring species. Uncertainties of predictive species distribution models are not equally distributed in space, and we would recommend accompanying maps of predicted distributions with a graphical representation of predictive performance.

KW - Environmental planning

KW - Commission

KW - Europe

KW - False absence rate

KW - False presence rate

KW - Omission

KW - Species distribution models

KW - Validation

UR - http://www.scopus.com/inward/record.url?scp=79961208544&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/0fbc8824-e827-3c5d-8b7a-ee49b05858d2/

U2 - 10.1111/j.1466-8238.2011.00649.x

DO - 10.1111/j.1466-8238.2011.00649.x

M3 - Journal articles

VL - 20

SP - 779

EP - 788

JO - Global Ecology and Biogeography

JF - Global Ecology and Biogeography

SN - 1466-822X

IS - 5

ER -

Recently viewed

Publications

  1. Modeling of Logistic Processes in Assembly Areas
  2. Different kinds of interactive exercises with response analysis on the web
  3. A sensor fault detection scheme as a functional safety feature for DC-DC converters
  4. Harvesting information from captions for weakly supervised semantic segmentation
  5. Understanding the socio-technical aspects of low-code adoption for software development
  6. Introduction Mobile Digital Practices. Situating People, Things, and Data
  7. Fast, Fully Automated Analysis of Voriconazole from Serum by LC-LC-ESI-MS-MS with Parallel Column-Switching Technique
  8. Exact and approximate inference for annotating graphs with structural SVMs
  9. Exploration strategies, performance, and error consequences when learning a complex computer task
  10. Lessons learned for spatial modelling of ecosystem services in support of ecosystem accounting
  11. How to support synchronous net-based learning discourses
  12. Construct Objectification and De-Objectification in Organization Theory
  13. Development and validation of a method for the determination of trace alkylphenols and phthalates in the atmosphere
  14. Modeling and numerical simulation of multiscale behavior in polycrystals via extended crystal plasticity
  15. A fast sequential injection analysis system for the simultaneous determination of ammonia and phosphate
  16. Taking the pulse of Earth's tropical forests using networks of highly distributed plots
  17. Backstepping-based Input-Output Linearization of a Peltier Element for Ice Clamping using an Unscented Kalman Filter
  18. A simple nonlinear PD control for faster and high-precision positioning of servomechanisms with actuator saturation
  19. How, when and why do negotiators use reference points?
  20. A lyapunov approach in the derivative approximation using a dynamic system
  21. Hierarchical trait filtering at different spatial scales determines beetle assemblages in deadwood
  22. Transductive support vector machines for structured variables
  23. Training effects of two different unstable shoe constructions on postural control in static and dynamic testing situations
  24. Selecting and Adapting Methods for Analysis and Design in Value-Sensitive Digital Social Innovation Projects: Toward Design Principles
  25. Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory
  26. Intentionality
  27. Comparison of Odor Thresholds obtained by a Three Alternative Choice Procedure and by the Method of Limits
  28. How does Enterprise Architecture support the Design and Realization of Data-Driven Business Models?
  29. Introducing parametric uncertainty into a nonlinear friction model