disdat-package: Data for species distribution modeling
In rspatial/disdat: Data for Comparing Species Distribution Modeling Methods
disdat-package R Documentation
Data for species distribution modeling
Description
This package allows for easy use of a collection of datasets that can be used to compare species distribution models. There are data for 6 regions in the world, for a total of 226 anonymised species including birds, vascular plants, reptiles and bats. Each data set has presence-only (and optionally background) training data to build models, and presence/absence data to evaluate models.
The data were compiled and used by a species distribution modeling working group sponsored by the National Center for Ecological Analysis and Synthesis (NCEAS), at UC Santa Barbara, USA. Full details of the dataset are provided in the first publication listed below, from the NCEAS data group.
Details
The data are fully described in the first publication listed below, and also supplied with metadata on Open Science Framework (OSF). On the OSF site, rasters (gridded data) of all environmental data are also available for download.
Author(s)
Package by Robert J. Hijmans, Roozbeh Valavi, and Jane Elith. Data collation and processing by the NCEAS data group (see first reference below, and the manual package for specific datasets).
References
The main reference for the these data is:
Elith, J., Graham, C.H., Valavi, R., Abegg, M., Bruce, C., Ferrier, S., Ford, A., Guisan, A., Hijmans, R.J., Huettmann, F., Lohmann, L.G., Loiselle, B.A., Moritz, C., Overton, J.McC., Peterson, A.T., Phillips, S., Richardson, K., Williams, S., Wiser, S.K., Wohlgemuth, T. & Zimmermann, N.E., (2020). Presence-only and presence-absence data for comparing species distribution modeling methods. Biodiversity Informatics 15:69-80.
Other papers using these data include:
Dudík, M. & Phillips, S. J. (2009). Generative and Discriminative Learning with Unknown Labeling Bias. in Advances in Neural Information Processing Systems 21 (eds. Koller, D., Schuurmans, D., Bengio, Y. & Bottou, L.) 401-408. Curran Associates, Inc.
Dudík, M., Phillips, S. J. & Schapire, R. E. (2006). Correcting sample selection bias in maximum entropy density estimation. in Advances in Neural Information Processing Systems 18 (eds. Weiss, Y., Schölkopf, B. & Platt, J. C.) 323-330 (MIT Press).
Elith, J. & Leathwick, J. R. (2007). Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Diversity and Distributions 13, 165-175.
Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.McC., Peterson, A.T., Phillips, S.J., Richardson, K.S., Scachetti-Pereira, R., Schapire, R.E., Soberón, J., Williams, S., Wisz, M.S., Zimmermann, N.E. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151
Graham, C.H., Elith, J., Hijmans, R.J., Guisan, A., Peterson, A.T., Loiselle, B.A. (2008). The influence of spatial errors in species occurrence data used in distribution models. Journal of Applied Ecology 45, 239–247.
Guisan, A., Graham, C. H., Elith, J., Huettmann, F. & NCEAS Species Distribution Modelling Group (2007). Sensitivity of predictive species distribution models to change in grain size: insights from a multi-models experiment across five continents. Diversity and Distributions 13, 332-340.
Guisan, A., Zimmermann, N. E., Elith, J., Graham, C. H., Phillips, S. P., & Peterson, A. T. (2007). What matters for predicting the occurences of trees: techniques, data, or species' characteristics? Ecological Monographs 77, 615-530.
Hijmans, R. J. (2012). Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology 93, 679-688.
Phillips, S. J. & Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161-175.
Phillips, S. J. & Elith, J. (2010). POC-plots: calibrating species distribution models with presence-only data. Ecology 91, 2476-2484.
Phillips, S.J., Dudík, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J., Ferrier, S. (2009). Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19, 181–197.
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. (2017). Opening the black box: an open-source release of Maxent. Ecography 40, 887-893.
Valavi, R., Elith, J., Lahoz‐Monfort, J.J. & Guillera‐Arroita, G. (2023). Flexible species distribution modelling methods perform well on spatially separated testing data. Global Ecology and Biogeography, geb.13639.
Valavi, R., Elith, J., Lahoz‐Monfort, J.J. & Guillera‐Arroita, G. (2021). Modelling species presence‐only data with random forests. Ecography, 44, 1731–1742.
Valavi, R., Guillera‐Arroita, G., Lahoz‐Monfort, J.J. & Elith, J. (2022). Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code. Ecological Monographs, 92(1). 10.1002/ecm.1486
Wisz, M.S., Hijmans, R.J., Li, J., Peterson, A.T., Graham, C.H., Guisan, A., & NCEAS Species Distribution Modelling Group (2008). Effects of sample size on the performance of species distribution models. Diversity and Distributions 14, 763–773.
rspatial/disdat documentation built on Feb. 14, 2023, 4:27 a.m.
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Data for species distribution modeling
Description
This package allows for easy use of a collection of datasets that can be used to compare species distribution models. There are data for 6 regions in the world, for a total of 226 anonymised species including birds, vascular plants, reptiles and bats. Each data set has presence-only (and optionally background) training data to build models, and presence/absence data to evaluate models.
The data were compiled and used by a species distribution modeling working group sponsored by the National Center for Ecological Analysis and Synthesis (NCEAS), at UC Santa Barbara, USA. Full details of the dataset are provided in the first publication listed below, from the NCEAS data group.
Details
The data are fully described in the first publication listed below, and also supplied with metadata on Open Science Framework (OSF). On the OSF site, rasters (gridded data) of all environmental data are also available for download.
Author(s)
Package by Robert J. Hijmans, Roozbeh Valavi, and Jane Elith. Data collation and processing by the NCEAS data group (see first reference below, and the manual package for specific datasets).
References
The main reference for the these data is:
Elith, J., Graham, C.H., Valavi, R., Abegg, M., Bruce, C., Ferrier, S., Ford, A., Guisan, A., Hijmans, R.J., Huettmann, F., Lohmann, L.G., Loiselle, B.A., Moritz, C., Overton, J.McC., Peterson, A.T., Phillips, S., Richardson, K., Williams, S., Wiser, S.K., Wohlgemuth, T. & Zimmermann, N.E., (2020). Presence-only and presence-absence data for comparing species distribution modeling methods. Biodiversity Informatics 15:69-80.
Other papers using these data include:
Dudík, M. & Phillips, S. J. (2009). Generative and Discriminative Learning with Unknown Labeling Bias. in Advances in Neural Information Processing Systems 21 (eds. Koller, D., Schuurmans, D., Bengio, Y. & Bottou, L.) 401-408. Curran Associates, Inc.
Dudík, M., Phillips, S. J. & Schapire, R. E. (2006). Correcting sample selection bias in maximum entropy density estimation. in Advances in Neural Information Processing Systems 18 (eds. Weiss, Y., Schölkopf, B. & Platt, J. C.) 323-330 (MIT Press).
Elith, J. & Leathwick, J. R. (2007). Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Diversity and Distributions 13, 165-175.
Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.McC., Peterson, A.T., Phillips, S.J., Richardson, K.S., Scachetti-Pereira, R., Schapire, R.E., Soberón, J., Williams, S., Wisz, M.S., Zimmermann, N.E. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151
Graham, C.H., Elith, J., Hijmans, R.J., Guisan, A., Peterson, A.T., Loiselle, B.A. (2008). The influence of spatial errors in species occurrence data used in distribution models. Journal of Applied Ecology 45, 239–247.
Guisan, A., Graham, C. H., Elith, J., Huettmann, F. & NCEAS Species Distribution Modelling Group (2007). Sensitivity of predictive species distribution models to change in grain size: insights from a multi-models experiment across five continents. Diversity and Distributions 13, 332-340.
Guisan, A., Zimmermann, N. E., Elith, J., Graham, C. H., Phillips, S. P., & Peterson, A. T. (2007). What matters for predicting the occurences of trees: techniques, data, or species' characteristics? Ecological Monographs 77, 615-530.
Hijmans, R. J. (2012). Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology 93, 679-688.
Phillips, S. J. & Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161-175.
Phillips, S. J. & Elith, J. (2010). POC-plots: calibrating species distribution models with presence-only data. Ecology 91, 2476-2484.
Phillips, S.J., Dudík, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J., Ferrier, S. (2009). Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19, 181–197.
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. (2017). Opening the black box: an open-source release of Maxent. Ecography 40, 887-893.
Valavi, R., Elith, J., Lahoz‐Monfort, J.J. & Guillera‐Arroita, G. (2023). Flexible species distribution modelling methods perform well on spatially separated testing data. Global Ecology and Biogeography, geb.13639.
Valavi, R., Elith, J., Lahoz‐Monfort, J.J. & Guillera‐Arroita, G. (2021). Modelling species presence‐only data with random forests. Ecography, 44, 1731–1742.
Valavi, R., Guillera‐Arroita, G., Lahoz‐Monfort, J.J. & Elith, J. (2022). Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code. Ecological Monographs, 92(1). 10.1002/ecm.1486
Wisz, M.S., Hijmans, R.J., Li, J., Peterson, A.T., Graham, C.H., Guisan, A., & NCEAS Species Distribution Modelling Group (2008). Effects of sample size on the performance of species distribution models. Diversity and Distributions 14, 763–773.
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