Sampling bias mitigation for species occurrence modeling using machine learning methods
Tài liệu tham khảo
Aiello-Lammens, 2015, spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models, Ecography, 38, 541, 10.1111/ecog.01132
Anderson, 2011, Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent, Ecol. Model., 222, 2796, 10.1016/j.ecolmodel.2011.04.011
Banks-Leite, 2014, Assessing the utility of statistical adjustments for imperfect detection in tropical conservation science, J. Appl. Ecol., 51, 849, 10.1111/1365-2664.12272
Barbet-Massin, 2012, Selecting pseudo-absences for species distribution models: how, where and how many?, Methods Ecol. Evol., 3, 327, 10.1111/j.2041-210X.2011.00172.x
Beck, 2014, Spatial bias in the GBIF database and its effect on modeling species' geographic distributions, Ecol. Inform., 19, 10, 10.1016/j.ecoinf.2013.11.002
Bennett, 2013, Characterising performance of environmental models, Environ. Model. Softw., 40, 1, 10.1016/j.envsoft.2012.09.011
Boria, 2014, Spatial filtering to reduce sampling bias can improve the performance of ecological niche models, Ecol. Model., 275, 73, 10.1016/j.ecolmodel.2013.12.012
Brotons, 2004, Presence-absence versus presence-only modelling methods for predicting bird habitat suitability, Ecography, 27, 437, 10.1111/j.0906-7590.2004.03764.x
R Core Team, 2019
Elith, 2009, Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models, Ecography, 32, 66, 10.1111/j.1600-0587.2008.05505.x
Elith, 2010, The art of modelling range-shifting species, Methods Ecol. Evol., 1, 330, 10.1111/j.2041-210X.2010.00036.x
Field, 2016, How does choice of statistical method to adjust counts for imperfect detection affect inferences about animal abundance?, Methods Ecol. Evol., 7, 1282, 10.1111/2041-210X.12601
Fiske, 2011, Unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance, J. Stat. Softw., 43, 1, 10.18637/jss.v043.i10
Foody, 2020, Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification, Remote Sens. Environ., 239, 111630, 10.1016/j.rse.2019.111630
Fourcade, 2014, Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias, PLoS One, 9, 10.1371/journal.pone.0097122
Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 29, 1189, 10.1214/aos/1013203451
Friedman, 2000, Additive logistic regression: a statistical view of boosting, Ann. Stat., 28, 337, 10.1214/aos/1016218223
Gholami, 2018, Adversary models account for imperfect crime data: forecasting and planning against real-world poachers, 823
Greenwell
Guillera-Arroita, 2017, Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities, Ecography, 40, 281, 10.1111/ecog.02445
Guillera-Arroita, 2014, Ignoring imperfect detection in biological surveys is dangerous: a response to ‘fitting and interpreting occupancy models, PLoS One, 9, 10.1371/journal.pone.0099571
Hashemi, 2018, Weighted machine learning, Stat. Optimiz. Inform. Comput., 6, 497, 10.19139/soic.v6i4.479
Hijmans
Hijmans
Hutchinson, 2011, Incorporating boosted regression trees into ecological latent variable models
Kadmon, 2004, Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models, Ecol. Appl., 14, 401, 10.1890/02-5364
Kellner, 2014, Accounting for imperfect detection in ecology: a quantitative review, PLoS One, 9, 10.1371/journal.pone.0111436
Kramer-Schadt, 2013, The importance of correcting for sampling bias in MaxEnt species distribution models, Divers. Distrib., 19, 1366, 10.1111/ddi.12096
Kuhn, 2008, Building predictive models in R using the caret package, J. Stat. Softw., 28, 1, 10.18637/jss.v028.i05
Kuhn
Lahoz-Monfort, 2014, Imperfect detection impacts the performance of species distribution models, Glob. Ecol. Biogeogr., 23, 504, 10.1111/geb.12138
Lehmann, 2006
Lintz, 2013, Effect of inventory method on niche models: random versus systematic error, Ecol. Informatics, 18, 20, 10.1016/j.ecoinf.2013.05.001
MacKenzie, 2006, Modeling the probability of resource use: the effect of, and dealing with, detecting a species imperfectly, J. Wildl. Manag., 70, 367, 10.2193/0022-541X(2006)70[367:MTPORU]2.0.CO;2
Mengersen, 2017, Modelling imperfect presence data obtained by citizen science, Environmetrics, 28, 10.1002/env.2446
Merow, 2014, What do we gain from simplicity versus complexity in species distribution models?, Ecography, 37, 1267, 10.1111/ecog.00845
Phillips, 2009, Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data, Ecol. Appl., 19, 181, 10.1890/07-2153.1
Pontius, 2011, Death to kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment, Int. J. Remote Sens., 32, 4407, 10.1080/01431161.2011.552923
Reddy, 2003, Geographical sampling bias and its implications for conservation priorities in Africa, J. Biogeogr., 30, 1719, 10.1046/j.1365-2699.2003.00946.x
Royle, 2005, Modelling occurrence and abundance of species when detection is imperfect, Oikos, 110, 353, 10.1111/j.0030-1299.2005.13534.x
Ward, 2007, Modelling the potential geographic distribution of invasive ant species in New Zealand, Biol. Invasions, 9, 723, 10.1007/s10530-006-9072-y
Ward, 2009, Presence-only data and the EM algorithm, Biometrics, 65, 554, 10.1111/j.1541-0420.2008.01116.x
Welsh, 2013, Fitting and interpreting occupancy models, PLoS One, 8, 10.1371/journal.pone.0052015
Yackulic, 2013, Presence-only modelling using MAXENT: when can we trust the inferences?, Methods Ecol. Evol., 4, 236, 10.1111/2041-210x.12004
Zaniewski, 2002, Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns, Ecol. Model., 157, 261, 10.1016/S0304-3800(02)00199-0