CoChat
Sign Up
Cited by 456

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling

Sillero, Neftalí, Arenas-Castro, Salvador, Enriquez‐Urzelai, Urtzi, Vale, Cândida Gomes, Sousa-Guedes, Diana, Martínez-Freiría, Fernando, Real, Raimundo, Barbosa, A.Márcia·2021·Ecological Modelling
DOI 10.1016/j.ecolmodel.2021.109671
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.ecolmodel.2021.109671

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 295

  1. Abatzoglou, 2018, TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from, 1958-2015, Sci. Data, 5, 1, 10.1038/sdata.2017.191 · DOI
  2. Acevedo, 2012, Delimiting the geographical background in species distribution modelling, J. Biogeogr., 39, 1383, 10.1111/j.1365-2699.2012.02713.x · DOI
  3. Acevedo, 2012, Favourability: concept, distinctive characteristics and potential usefulness, Naturwissensch., 99, 515, 10.1007/s00114-012-0926-0 · DOI
  4. Ahmadzadeh, 2016, Separate histories in both sides of the mediterranean: phylogeny and niche evolution of ocellated lizards, J. Biogeogr., 43, 1242, 10.1111/jbi.12703 · DOI
  5. Aiello-Lammens, 2015, spThin: an r package for spatial thinning of species occurrence records for use in ecological niche models, Ecograph. (Cop.), 38, 1
  6. Allouche, 2006, Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (tss), J. Appl. Ecol., 6, 1223, 10.1111/j.1365-2664.2006.01214.x · DOI · Paper page
  7. Anderson, 2013, Lightweight unmanned aerial vehicles will revolutionize spatial ecology, Front. Ecol. Environ., 11, 138, 10.1890/120150 · DOI
  8. Anderson, 2015, El modelado de nichos y distribuciones: no es simplemente “clic, clic, clic, Biogeografía, 8, 4
  9. Anderson, 2013, A framework for using niche models to estimate impacts of climate change on species distributions, Ann. N. Y. Acad. Sci., 1297, 8, 10.1111/nyas.12264 · DOI
  10. Anderson, 2012, Harnessing the world's biodiversity data: promise and peril in ecological niche modeling of species distributions, Ann. N. Y. Acad. Sci., 1260, 66, 10.1111/j.1749-6632.2011.06440.x · DOI
  11. Anderson, 2010, The effect of the extent of the study region on gis models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus nephelomys) in venezuela, J. Biogeogr., 37, 1378, 10.1111/j.1365-2699.2010.02290.x · DOI
  12. Andrade, 2020, ENMTML: an R package for a straightforward construction of complex ecological niche models, Environ. Model. Softw., 125, 10.1016/j.envsoft.2019.104615 · DOI
  13. Araújo, 2019, Standards for distribution models in biodiversity assessments, Sci. Adv., 5, eaat4858, 10.1126/sciadv.aat4858 · DOI
  14. Araujo, 2007, Ensemble forecasting of species distributions, Trend. Ecol. Evol., 22, 42, 10.1016/j.tree.2006.09.010 · DOI · Paper page
  15. Araújo, 2012, Uses and misuses of bioclimatic envelope modeling, Ecol., 93, 1527, 10.1890/11-1930.1 · DOI
  16. Areias-Guerreiro, 2016, How well can models predict changes in species distributions? a 13-year-old otter model revisited data gathering, Hystrix, Ital. J. Mammal., 1
  17. Arenas-Castro, 2021, SDM-CropProj – A model-assisted framework to forecast crop environmental suitability and fruit production, MethodsX, 8, 10.1016/j.mex.2021.101394 · DOI
  18. Arenas-Castro, 2018, Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling, PLoS ONE, 13, 1, 10.1371/journal.pone.0199292 · DOI
  19. Arenas-Castro, 2020, Projected climate changes are expected to decrease the suitability and production of olive varieties in southern Spain, Sci. Tot. Environ., 709, 10.1016/j.scitotenv.2019.136161 · DOI
  20. Arenas-Castro, 2019, Remotely sensed variables of ecosystem functioning support robust predictions of abundance patterns for rare species, Rem. Sens., 11, 2086, 10.3390/rs11182086 · DOI
  21. Arenas-Castro, 2021, Cross-scale monitoring of habitat suitability changes using satellite time series and ecological niche models, Sci. Tot. Environ., 784, 10.1016/j.scitotenv.2021.147172 · DOI
  22. Arntzen, 2008, Environmental parameters that determine species geographical range limits as a matter of time and space, J. Biogeogr., 7, 1177, 10.1111/j.1365-2699.2007.01875.x · DOI
  23. Aubry, 2017, The importance of data quality for generating reliable distribution models for rare, elusive, and cryptic species, PLoS ONE, 12, 1, 10.1371/journal.pone.0179152 · DOI
  24. Báez, 2007, Loggerhead turtle by-catch depends on distance to the coast, independent of fishing effort: implications for conservation and fisheries management, Mar. Ecol. Prog. Ser., 338, 249, 10.3354/meps338249 · DOI
  25. Báez, 2020, Ensemble modeling of the potential distribution of the whale shark in the atlantic ocean, Ecol. Evol., 10, 175, 10.1002/ece3.5884 · DOI
  26. Barbet-Massin, 2012, Selecting pseudo-absences for species distribution models: how, where and how many?, Meth. Ecol. Evol., 3, 327, 10.1111/j.2041-210X.2011.00172.x · DOI · Paper page
  27. Barbet-Massin, 2010, How much do we overestimate future local extinction rates when restricting the range of occurrence data in climate suitability models?, Ecograph. (Cop.), 33, 878, 10.1111/j.1600-0587.2010.06181.x · DOI
  28. Barbosa, 2013, New measures for assessing model equilibrium and prediction mismatch in species distribution models, Dive.. Distrib., 19, 1333, 10.1111/ddi.12100 · DOI
  29. Barbosa, 2003, Otter (lutra lutra) distribution modeling at two resolution scales suited to conservation planning in the iberian peninsula, Biol. Conserv., 3, 377, 10.1016/S0006-3207(03)00066-1 · DOI
  30. Barve, 2011, The crucial role of the accessible area in ecological niche modeling and species distribution modeling, Ecol. Modell., 222, 1810, 10.1016/j.ecolmodel.2011.02.011 · DOI
  31. Batllori, 2013, Climate change-induced shifts in fire for mediterranean ecosystems, Glob. Ecol. Biogeogr., 22, 1118, 10.1111/geb.12065 · DOI · Paper page
  32. Beale, 2008, Opening the climate envelope reveals no macroscale associations with climate in european birds, Proc. Natl. Acad. Sci. U. S. A., 105, 14908, 10.1073/pnas.0803506105 · DOI
  33. Beaumont, 2005, Predicting species distributions: use of climatic parameters in bioclim and its impact on predictions of species’ current and future distributions, Ecol. Modell., 2, 251, 10.1016/j.ecolmodel.2005.01.030 · DOI
  34. 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 · DOI
  35. Bedia, 2013, Dangers of using global bioclimatic datasets for ecological niche modeling. limitations for future climate projections, Glob. Planet. Chang., 107, 1, 10.1016/j.gloplacha.2013.04.005 · DOI
  36. Berger, 2012, The sentinel missions-new opportunities for science, Rem. Sens. Environ., 120, 1, 10.1016/j.rse.2011.12.026 · DOI
  37. Blonder, 2014, The n-dimensional hypervolume, Glob. Ecol. Biogeogr., 23, 595, 10.1111/geb.12146 · DOI
  38. Bohl, 2019, A new null model approach to quantify performance and significance for ecological niche models of species distributions, J. Biogeogr., 46, 1101, 10.1111/jbi.13573 · DOI
  39. Booth, 2014, BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current maxent studies, Divers. Distrib., 20, 1, 10.1111/ddi.12144 · DOI
  40. Boria, 2014, Spatial filtering to reduce sampling bias can improve the performance of ecological niche models, Ecol. Modell., 275, 73, 10.1016/j.ecolmodel.2013.12.012 · DOI
  41. Boyce, 2002, Evaluating resource selection functions, Ecol. Modell., 2–3, 281, 10.1016/S0304-3800(02)00200-4 · DOI
  42. Bradley, 1997, The use of the area under the roc curve in the evaluation of machine learning algorithms, Patte. Recognit., 30, 1145, 10.1016/S0031-3203(96)00142-2 · DOI · Paper page
  43. Breiman, 1999, Random forest, Mach. Learn., 45, 1
  44. Breiner, 2015, Overcoming limitations of modelling rare species by using ensembles of small models, Meth. Ecol. Evol., 6, 1210, 10.1111/2041-210X.12403 · DOI
  45. Breiner, 2018, Optimizing ensembles of small models for predicting the distribution of species with few occurrences, Meth. Ecol. Evol., 2018, 1
  46. Briscoe, 2016, Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia, Glob. Chang. Biol., 22, 2425, 10.1111/gcb.13280 · DOI
  47. Brito, 1999, Modelling wildlife distributions: logistic multiple regression vs overlap analysis, Ecograph. (Cop.), 3, 251, 10.1111/j.1600-0587.1999.tb00500.x · DOI
  48. Broennimann, 2012, Measuring ecological niche overlap from occurrence and spatial environmental data, Glob. Ecol. Biogeogr., 21, 481, 10.1111/j.1466-8238.2011.00698.x · DOI
  49. Brown, 2014, SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses, Meth. Ecol. Evol., 5, 694, 10.1111/2041-210X.12200 · DOI
  50. Buckley, 2018, Leveraging organismal biology to forecast the effects of climate change, Integr. Comp. Biol., 58, 38, 10.1093/icb/icy018 · DOI
  51. Buisson, 2010, Uncertainty in ensemble forecasting of species distribution, Glob. Chang. Biol., 16, 1145, 10.1111/j.1365-2486.2009.02000.x · DOI
  52. Cabral, 2017, Mechanistic simulation models in macroecology and biogeography: state-of-art and prospects, Ecograph. (Cop.), 40, 267, 10.1111/ecog.02480 · DOI
  53. Carpenter, 1993, DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals, Biodivers. Conserv., 6, 667, 10.1007/BF00051966 · DOI
  54. Carretero, 2016, Evaluating how species niche modelling is affected by partial distributions with an empirical case, Acta Oecologic., 77, 207, 10.1016/j.actao.2016.08.014 · DOI
  55. Carvalho, 2011, Conservation planning under climate change: toward accounting for uncertainty in predicted species distributions to increase confidence in conservation investments in space and time, Biol. Conserv., 144, 2020, 10.1016/j.biocon.2011.04.024 · DOI
  56. Carvalho, 2010, From climate change predictions to actions- conserving vulnerable animal groups in hotspots at a regional scale, Glob. Chang. Biol., 16, 3257, 10.1111/j.1365-2486.2010.02212.x · DOI
  57. Carvalho, 2010, Simulating the effects of using different types of species distribution data in reserve selection, Biol. Conserv., 143, 426, 10.1016/j.biocon.2009.11.010 · DOI
  58. Castro, 2008, Modelling the spatial distribution of the tengmalm’s owl aegolius funereus in its southwestern palaeartic limit (ne spain), Ardeola, 55, 71
  59. Chamorro, 2020, Fuzzy sets allow gaging the extent and rate of species range shift due to climate change, Sci. Rep., 10, 1, 10.1038/s41598-020-73509-y · DOI
  60. Chapman, 2019, Improving species distribution models for invasive non-native species with biologically informed pseudo-absence selection, J. Biogeogr., 46, 1029, 10.1111/jbi.13555 · DOI
  61. Chefaoui, 2016, Large-Scale prediction of seagrass distribution integrating landscape metrics and environmental factors: the case of cymodocea nodosa (mediterranean–atlantic), Estuari. Coast., 39, 123, 10.1007/s12237-015-9966-y · DOI
  62. Chefaoui, 2008, Assessing the effects of pseudo-absences on predictive distribution model performance, Ecol. Modell., 4, 478, 10.1016/j.ecolmodel.2007.08.010 · DOI
  63. Chiarenza, 2019, Ecological niche modelling does not support climatically-driven dinosaur diversity decline before the cretaceous/paleogene mass extinction, Nat. Commun., 10, 1091, 10.1038/s41467-019-08997-2 · DOI
  64. Clark, 1993, A multivariate model of female black bear habitat use for a Geographic Information System, J. Wildl. Manage., 57, 519, 10.2307/3809276 · DOI
  65. Clark, 1954, Distance to nearest neighbor as a measure of spatial relationships in populations, Ecol., 35, 445, 10.2307/1931034 · DOI
  66. Comrie, 1999, Principal components-based regionalization of precipitation regimes across the southwest united states and northern mexico, with an application to monsoon precipitation variability, Clim. Res., 10, 201, 10.3354/cr010201 · DOI
  67. Cord, 2013, Modelling species distributions with remote sensing data: bridging disciplinary perspectives, J. Biogeogr., 40, 2226, 10.1111/jbi.12199 · DOI
  68. Costa, 2015, Impacts of species misidentification on species distribution modeling with presence-only data, ISPRS. Int. J. Geo-Information, 4, 2496, 10.3390/ijgi4042496 · DOI
  69. Cumming, 2002, Comparing climate and vegetation as limiting factors for species ranges of african ticks, Ecol., 1, 255, 10.1890/0012-9658(2002)083[0255:CCAVAL]2.0.CO;2 · DOI · Paper page
  70. Cumming, 2000, Using between-model comparisons to fine-tune linear models of species ranges, J. Biogeogr., 2, 441, 10.1046/j.1365-2699.2000.00408.x · DOI
  71. Dallas, 2018, Habitat suitability estimated by niche models is largely unrelated to species abundance, Glob. Ecol. Biogeogr., 27, 1448, 10.1111/geb.12820 · DOI
  72. De Marco, 2018, Evaluating collinearity effects on species distribution models: an approach based on virtual species simulation, PLoS ONE, 13, 10.1371/journal.pone.0202403 · DOI
  73. Di Cola, 2017, Ecospat: an r package to support spatial analyses and modeling of species niches and distributions, Ecograph. (Cop.), 40, 774, 10.1111/ecog.02671 · DOI
  74. Dicko, 2014, Using species distribution models to optimize vector control in the framework of the tsetse eradication campaign in senegal, Proc. Natl. Acad. Sci, 1–6
  75. Dormann, 2013, Collinearity: a review of methods to deal with it and a simulation study evaluating their performance, Ecograph. (Cop.), 36, 27, 10.1111/j.1600-0587.2012.07348.x · DOI · Paper page
  76. Dormann, 2012, Correlation and process in species distribution models: bridging a dichotomy, J. Biogeogr., 39, 2119, 10.1111/j.1365-2699.2011.02659.x · DOI
  77. Dudík, 2005, Correcting sample selection bias in maximum entropy density estimation, Adv. Neural Inf. Process. Syst., 323
  78. Elith, 2009, Species distribution models: ecological explanation and prediction across space and time, Annu. Rev. Ecol. Evol. Syst., 40, 677, 10.1146/annurev.ecolsys.110308.120159 · DOI · Paper page
  79. Elith, 2008, A working guide to boosted regression trees, J. Anim. Ecol., 77, 802, 10.1111/j.1365-2656.2008.01390.x · DOI · Paper page
  80. Elith, 2011, A statistical explanation of maxent for ecologists, Divers. Distrib., 17, 43, 10.1111/j.1472-4642.2010.00725.x · DOI · Paper page
  81. Engler, 2009, MigClim: predicting plant distribution and dispersal in a changing climate, Divers. Distrib., 15, 590, 10.1111/j.1472-4642.2009.00566.x · DOI
  82. Engler, 2004, An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data, J. Appl. Ecol., 41, 263, 10.1111/j.0021-8901.2004.00881.x · DOI
  83. Engler, 2012, The migclim r package - seamless integration of dispersal constraints into projections of species distribution models, Ecograph. (Cop.), 35, 872, 10.1111/j.1600-0587.2012.07608.x · DOI
  84. Enrique, 2009, OpenModeller: a generic approach to species ’ potential distribution modelling, Geoinformati., 1
  85. Enriquez-Urzelai, 2020, The roles of acclimation and behaviour in buffering climate change impacts along elevational gradients, J. Anim. Ecol., 89, 1722, 10.1111/1365-2656.13222 · DOI
  86. Enriquez-Urzelai, 2019, Integrating mechanistic and correlative niche models to unravel range-limiting processes in a temperate amphibian, Glob. Chang. Biol., 1
  87. Espregueira Themudo, 2009, Multiple nuclear and mitochondrial genes resolve the branching order of a rapid radiation of crested newts (triturus, salamandridae), Mol. Phylogenet. Evol., 2, 321, 10.1016/j.ympev.2009.03.024 · DOI
  88. Estrada, 2018, Changes in potential mammal diversity in national parks and their implications for conservation, Curr. Zool., 64, 671
  89. Fawcett, 2006, An introduction to roc analysis, Patte. Recognit. Lett., 27, 861, 10.1016/j.patrec.2005.10.010 · DOI · Paper page
  90. Feller, W., 1968. An Introduction to Probability Theory and Its Applications. Popul. (French Ed. 10.2307/1526422. · DOI
  91. Feng, 2019, Collinearity in ecological niche modeling: confusions and challenges, Ecol. Evol., ece3.5555
  92. Feng, 2019, A checklist for maximizing reproducibility of ecological niche models, Nat. Ecol. Evol., 3, 10.1038/s41559-019-0972-5 · DOI · Paper page
  93. Fernandes, 2019, Effects of simulated observation errors on the performance of species distribution models, Divers. Distrib., 25, 400, 10.1111/ddi.12868 · DOI
  94. Ferrier, 2002, Extended statistical approaches to modelling spatial pattern in biodiversity in northeast new southwales. i. species-level modelling, Biodivers. Conserv., 2275, 10.1023/A:1021302930424 · DOI
  95. Ficetola, 2007, Prediction and validation of the potential global distribution of a problematic alien invasive species: the american bullfrog, Divers. Distrib., 13, 476, 10.1111/j.1472-4642.2007.00377.x · DOI
  96. Fick, 2017, WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 37, 4302, 10.1002/joc.5086 · DOI · Paper page
  97. Fielding, 1997, A review of methods for the assessment of prediction errors in conservation presence/absence models, Environ. Conserv., 24, 38, 10.1017/S0376892997000088 · DOI · Paper page
  98. Fithian, 2015, Bias correction in species distribution models: pooling survey and collection data for multiple species, Meth. Ecol. Evol., 6, 424, 10.1111/2041-210X.12242 · DOI
  99. 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 · DOI · Paper page
  100. Franch, 2015, Temporal analysis of mauremys leprosa (testudines, geoemydidae) distribution in northeastern iberia: unusual increase in the distribution of a native species, Hydrobiologia., 757, 129, 10.1007/s10750-015-2247-8 · DOI
  101. Freeman, 2008, A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa, Ecol. Modell., 1–2, 48, 10.1016/j.ecolmodel.2008.05.015 · DOI
  102. Fréjaville, 2018, The eumedclim database: yearly climate data (1901–2014) of 1 km resolution grids for europe and the mediterranean basin, Front. Ecol. Evol., 6, 1, 10.3389/fevo.2018.00031 · DOI
  103. Gallien, 2012, Invasive species distribution models - how violating the equilibrium assumption can create new insights, Glob. Ecol. Biogeogr., 21, 1126, 10.1111/j.1466-8238.2012.00768.x · DOI
  104. Getis, 2008, A history of the concept of spatial autocorrelation: a geographer ’s perspective, Geogr. Anal., 40, 297, 10.1111/j.1538-4632.2008.00727.x · DOI
  105. Getis, 2007, Reflections on spatial autocorrelation, Reg. Sci. Urban Econ., 37, 491, 10.1016/j.regsciurbeco.2007.04.005 · DOI
  106. Graham, 2007, The influence of spatial errors in species occurrence data used in distribution models, J. Appl. Ecol., 45, 239, 10.1111/j.1365-2664.2007.01408.x · DOI
  107. Grinnell, 1917, The niche-relationships of the california thrasher, Auk, 34, 427, 10.2307/4072271 · DOI
  108. Guillera-Arroita, 2014, Maxent is not a presence-absence method: a comment on Thibaud et al., Meth. Ecol. Evol., 5, 1192, 10.1111/2041-210X.12252 · DOI
  109. Guillera-Arroita, 2015, Is my species distribution model fit for purpose? matching data and models to applications, Glob. Ecol. Biogeogr., 24, 276, 10.1111/geb.12268 · DOI
  110. Guisan, 2002, Generalized linear and generalized additive models in studies of species distributions: setting the scene, Ecol. Modell., 89, 10.1016/S0304-3800(02)00204-1 · DOI · Paper page
  111. Guisan, 2011, SESAM - a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages, J. Biogeogr., 38, 1433, 10.1111/j.1365-2699.2011.02550.x · DOI
  112. Guisan, 2005, Predicting species distribution: offering more than simple habitat models, Ecol. Lett., 9, 993, 10.1111/j.1461-0248.2005.00792.x · DOI
  113. Guisan, A., Thuiller, W., Zimmermann, N.E., 2017. Habitat Suitability and Distribution models: With applications in R, Habitat Suitability and Distribution Models: With Applications in R. 10.1017/9781139028271. · DOI
  114. Guisan, 1999, GLM versus cca spatial modeling of plant species distribution, Plant Ecol., 107, 10.1023/A:1009841519580 · DOI
  115. Guisan, 2000, Predictive habitat distribution models in ecology, Ecol. Modell., 135, 147, 10.1016/S0304-3800(00)00354-9 · DOI · Paper page
  116. Guo, 2010, ModEco: an integrated software package for ecological niche modeling, Ecograph. (Cop, 4, 637, 10.1111/j.1600-0587.2010.06416.x · DOI
  117. Hampe, 2004, Bioclimate envelope models: what they detect and what they hide, Glob. Ecol. Biogeogr., 13, 469, 10.1111/j.1466-822X.2004.00090.x · DOI
  118. Hanley, 1982, The meaning and use of the area under a receiver operating characteristic (roc) curve, Radiol., 29, 10.1148/radiology.143.1.7063747 · DOI · Paper page
  119. Hao, 2019, A review of evidence about use and performance of species distribution modelling ensembles like biomod, Divers. Distrib., 25, 839, 10.1111/ddi.12892 · DOI
  120. Hao, 2020, Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models, Ecograph. (Cop.), 43, 549, 10.1111/ecog.04890 · DOI
  121. Harisena, 2021, When is variable importance estimation in species distribution modelling affected by spatial correlation?, Ecograph. (Cop.), 44, 778
  122. Harris, 2014, Updated high-resolution grids of monthly climatic observations - the cru ts3.10 dataset, Int. J. Climatol., 34, 623, 10.1002/joc.3711 · DOI
  123. Hatten, 2014, Mapping and monitoring mount graham red squirrel habitat with lidar and landsat imagery, Ecol. Modell., 289, 106, 10.1016/j.ecolmodel.2014.07.004 · DOI
  124. He, 2015, Will remote sensing shape the next generation of species distribution models?, Rem. Sens. Ecol. Conserv., 1, 4, 10.1002/rse2.7 · DOI
  125. Heisey, 2010, Linking process to pattern: estimating spatiotemporal dynamics of a wildlife epidemic from cross-sectional data, Ecol. Monogr., 80, 221, 10.1890/09-0052.1 · DOI
  126. Hemami, 2018, Using ecological models to explore niche partitioning within a guild of desert felids, Hystrix, 29
  127. Hernandez, 2008, Predicting species distributions in poorly-studied landscapes, Biodivers. Conserv., 17, 1353, 10.1007/s10531-007-9314-z · DOI
  128. Hernandez, 2006, The effect of sample size and species characteristics on performance of different species distribution modeling methods, Ecograph. (Cop.), 29, 773, 10.1111/j.0906-7590.2006.04700.x · DOI
  129. Hertzog, 2014, Field validation shows bias-corrected pseudo-absence selection is the best method for predictive species-distribution modelling, Divers. Distrib., 20, 1403, 10.1111/ddi.12249 · DOI
  130. Hijmans, 2012, Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model, Ecol., 93, 679, 10.1890/11-0826.1 · DOI
  131. Hijmans, 2005, Very high resolution interpolated climate surfaces for global land areas, Int. J. Climatol., 25, 1965, 10.1002/joc.1276 · DOI
  132. Hirzel, 2002, Which is the optimal sampling strategy for habitat suitability modelling?, Ecol. Modell., 2–3, 331, 10.1016/S0304-3800(02)00203-X · DOI
  133. Hirzel, 2002, Ecological-niche factor analysis: how to compute habitat suitability maps without absence-data?, Ecol., 7, 2027, 10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2 · DOI
  134. Hirzel, 2006, Evaluating the ability of habitat suitability models to predict species presences: predicting species distributions - results from a second workshop on advances in predictive species distribution models, held in riederalp, switzerland, 2004, Ecol. Modell., 2, 142, 10.1016/j.ecolmodel.2006.05.017 · DOI
  135. Holt, 2003, On the evolutionary ecology of species’ ranges, Evol. Ecol. Res., 5, 159
  136. Ikeda, 2017, Genetically informed ecological niche models improve climate change predictions, Glob. Chang. Biol., 23, 164, 10.1111/gcb.13470 · DOI
  137. Iturbide, 2015, A framework for species distribution modelling with improved pseudo-absence generation, Ecol. Modell., 312, 166, 10.1016/j.ecolmodel.2015.05.018 · DOI
  138. Jackson, 2000, Responses of plant populations and communities to environmental changes of the late quaternary, Paleobiol., 4, 194, 10.1666/0094-8373(2000)26[194:ROPPAC]2.0.CO;2 · DOI
  139. Jarnevich, 2015, Caveats for correlative species distribution modeling, Ecol. Inform., 29, 6, 10.1016/j.ecoinf.2015.06.007 · DOI
  140. Jarnevich, 2017, Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: an example with background selection, Ecol. Modell., 363, 48, 10.1016/j.ecolmodel.2017.08.017 · DOI
  141. Jiménez-Valverde, 2013, Discrimination capacity in species distribution models depends on the representativeness of the environmental domain, Glob. Ecol. Biogeogr., 22, 508, 10.1111/geb.12007 · DOI
  142. Jiménez-Valverde, 2009, The effect of prevalence and its interaction with sample size on the reliability of species distribution models, Communit. Ecol., 10, 196, 10.1556/ComEc.10.2009.2.9 · DOI
  143. Jimenez-Valverde, 2006, The ghost of unbalanced species distribution data in geographical model predictions, Divers. Distrib., 12, 521, 10.1111/j.1366-9516.2006.00267.x · DOI
  144. Jiménez-Valverde, 2007, Threshold criteria for conversion of probability of species presence to either-or presence-absence, Acta Oecologic., 31, 361, 10.1016/j.actao.2007.02.001 · DOI
  145. Jiménez-Valverde, 2008, Not as good as they seem: the importance of concepts in species distribution modelling, Divers. Distrib., 6, 885, 10.1111/j.1472-4642.2008.00496.x · DOI
  146. Kadmon, 2004, Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models, Ecol. Appl., 14, 401, 10.1890/02-5364 · DOI
  147. Kanagaraj, 2013, Modelling species distributions to map the road towards carnivore conservation in the tropics, Raffl. Bull. Zool., 85
  148. Karger, 2017, Climatologies at high resolution for the earth's land surface areas, Sci. Data, 4, 1, 10.1038/sdata.2017.122 · DOI
  149. Kearney, 2009, Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges, Ecol. Lett., 4, 334, 10.1111/j.1461-0248.2008.01277.x · DOI
  150. Kearney, 2009, Integrating biophysical models and evolutionary theory to predict climatic impacts on species’ ranges: the dengue mosquito aedes aegypti in australia, Funct. Ecol., 3, 528, 10.1111/j.1365-2435.2008.01538.x · DOI
  151. Kearney, 2010, Modelling the ecological niche from functional traits, Philos. Trans. R. Soc. B Biol. Sci., 365, 3469, 10.1098/rstb.2010.0034 · DOI
  152. Kearney, 2020, NicheMapR – an r package for biophysical modelling: the ectotherm and dynamic energy budget models, Ecograph. (Cop.), 43, 85, 10.1111/ecog.04680 · DOI
  153. Khalatbari, 2018, Availability of prey and natural habitats are related with temporal dynamics in range and habitat suitability for asiatic cheetah, Hystrix, Ital. J. Mammal., 29, 145
  154. Kolbe, 2010, Modeling the consequences of thermal trait variation for the cane toad invasion of australia, Ecol. Appl., 20, 2273, 10.1890/09-1973.1 · DOI
  155. Kramer-Schadt, 2013, The importance of correcting for sampling bias in maxent species distribution models, Divers. Distrib., 19, 1366, 10.1111/ddi.12096 · DOI · Paper page
  156. Kriticos, 2010, The effects of climate data precision on fitting and projecting species niche models, Ecograph. (Cop.), 1, 115, 10.1111/j.1600-0587.2009.06042.x · DOI
  157. Kriticos, 2012, CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling, Meth. Ecol. Evol., 3, 53, 10.1111/j.2041-210X.2011.00134.x · DOI
  158. Kutner, M.H., Nachtsheim, C.J., Neter, J., Li, W., 2004. Applied linear statistical models fifth edition.
  159. Legendre, 1993, Spatial autocorralation: trouble or new paradigm?, Ecol., 74, 1659, 10.2307/1939924 · DOI
  160. Leroy, 2018, Without quality presence–absence data, discrimination metrics such as tss can be misleading measures of model performance, J. Biogeogr., 45, 1994, 10.1111/jbi.13402 · DOI
  161. Levins, 1969, Some demographic and genetic consequences of environmental heterogeneity for, Biologic. Contr. Bull. Entomol. Soc. Am., 15, 237
  162. Liu, 2005, Selecting thresholds of occurrence in the prediction of species distributions, Ecograph. (Cop.), 28, 385, 10.1111/j.0906-7590.2005.03957.x · DOI · Paper page
  163. Liu, 2016, On the selection of thresholds for predicting species occurrence with presence-only data, Ecol. Evol., 6, 337, 10.1002/ece3.1878 · DOI
  164. Liu, 2013, Selecting thresholds for the prediction of species occurrence with presence-only data, J. Biogeogr., 40, 778, 10.1111/jbi.12058 · DOI
  165. Liu, 2011, Measuring and comparing the accuracy of species distribution models with presence-absence data, Ecograph. (Cop.), 34, 232, 10.1111/j.1600-0587.2010.06354.x · DOI
  166. Lobo, 2010, The uncertain nature of absences and their importance in species distribution modelling, Ecograph. (Cop.), 33, 103, 10.1111/j.1600-0587.2009.06039.x · DOI · Paper page
  167. Lobo, 2008, AUC: a misleading measure of the performance of predictive distribution models, Glob. Ecol. Biogeogr., 17, 145, 10.1111/j.1466-8238.2007.00358.x · DOI · Paper page
  168. Lobo, 2011, Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data, J. Nat. Conserv., 19, 1, 10.1016/j.jnc.2010.03.002 · DOI
  169. Maguire, 1973, Niche response structure and the analytical potentials of its relationship to the habitat, Am. Nat., 107, 213, 10.1086/282827 · DOI
  170. Manel, 2001, Evaluating presence/absence models in ecology: the need to account for prevalence, J. Appl. Ecol., 38, 921, 10.1046/j.1365-2664.2001.00647.x · DOI · Paper page
  171. Marmion, 2009, Evaluation of consensus methods in predictive species distribution modelling, Divers. Distrib., 15, 59, 10.1111/j.1472-4642.2008.00491.x · DOI
  172. Martínez-Freiría, 2013, Climate change is predicted to negatively influence moroccan endemic reptile richness. implications for conservation in protected areas, Naturwissenschaf., 100, 877, 10.1007/s00114-013-1088-4 · DOI
  173. Martínez-Freiría, 2017, Integrative phylogeographical and ecological analysis reveals multiple pleistocene refugia for mediterranean daboia vipers in north-west africa, Biol. J. Linn. Soc., 122, 366, 10.1093/biolinnean/blx038 · DOI
  174. Martínez-Freiría, 2020, Climatic refugia boosted allopatric diversification in western mediterranean vipers, J. Biogeogr., 47, 1698, 10.1111/jbi.13861 · DOI
  175. Martínez-Freiría, 2008, GIS-based niche models identify environmental correlates sustaining a contact zone between three species of european vipers, Divers. Distrib., 14, 452, 10.1111/j.1472-4642.2007.00446.x · DOI
  176. Martínez-Freiría, 2016, Contemporary niche contraction affects climate change predictions for elephants and giraffes, Divers. Distrib., 22, 432, 10.1111/ddi.12406 · DOI
  177. Martínez-Freiría, 2015, Trapped by climate: interglacial refuge and recent population expansion in the endemic iberian adder vipera seoanei, Divers. Distrib., 21, 331, 10.1111/ddi.12265 · DOI
  178. Martínez-Meyer, 2006, Conservatism of ecological niche characteristics in north american plant species over the pleistocene-to-recent transition, J. Biogeogr., 10, 1779, 10.1111/j.1365-2699.2006.01482_33_10.x · DOI
  179. Martínez-Meyer, 2004, Ecological niches as stable distributional constraints on mammal species, with implications for pleistocene extinctions and climate change projections for biodiversity, Glob. Ecol. Biogeogr., 4, 305, 10.1111/j.1466-822X.2004.00107.x · DOI
  180. Mason, 2002, Areas beneath the relative operating characteristics (roc) and relative operating levels (rol) curves: statistical significance and interpretation, Q. J. R. Meteorol. Soc., 128, 10.1256/003590002320603584 · DOI
  181. Merow, 2013, A practical guide to maxent for modeling species’ distributions: what it does, and why inputs and settings matter, Ecograph. (Cop.), 36, 1058, 10.1111/j.1600-0587.2013.07872.x · DOI · Paper page
  182. Mesgaran, 2014, Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models, Divers. Distrib., 20, 10.1111/ddi.12209 · DOI
  183. Meyer, 2016, Range geometry and socio-economics dominate species-level biases in occurrence information, Glob. Ecol. Biogeogr., 25, 1181, 10.1111/geb.12483 · DOI
  184. Meynard, 2007, Predicting species distributions: a critical comparison of the most common statistical models using artificial species, J. Biogeogr., 8, 1455, 10.1111/j.1365-2699.2007.01720.x · DOI
  185. Miller, 1991, Validation techniques for logistic regression models, Stat. Med., 10, 1213, 10.1002/sim.4780100805 · DOI
  186. Mitchell, 2017, Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes, Meth. Ecol. Evol., 8, 12, 10.1111/2041-210X.12645 · DOI
  187. Moisen, 2002, Comparing five modelling techniques for predicting forest characteristics, Ecol. Modell., 2–3, 209, 10.1016/S0304-3800(02)00197-7 · DOI
  188. Moran, 1950, Notes on continuous stochastic phenomena, Biometrik., 37, 17, 10.1093/biomet/37.1-2.17 · DOI · Paper page
  189. Moran, 2016, Intraspecific trait variation across scales: implications for understanding global change responses, Glob. Chang. Biol., 22, 137, 10.1111/gcb.13000 · DOI
  190. Muñoz, 2006, Assessing the potential range expansion of the exotic monk parakeet in spain, Divers. Distrib., 6, 656, 10.1111/j.1472-4642.2006.00272.x · DOI
  191. Muscarella, 2014, ENMeval: an r package for conducting spatially independent evaluations and estimating optimal model complexity for maxent ecological niche models, Meth. Ecol. Evol., 5, 1198, 10.1111/2041-210X.12261 · DOI · Paper page
  192. Naimi, 2016, sdm: a reproducible and extensible r platform for species distribution modelling, Ecograph. (Cop.), 39, 368, 10.1111/ecog.01881 · DOI
  193. Naimi, 2014, Where is positional uncertainty a problem for species distribution modelling?, Ecograph. (Cop.), 37, 191, 10.1111/j.1600-0587.2013.00205.x · DOI
  194. Naimi, 2011, Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling, J. Biogeogr., 38, 1497, 10.1111/j.1365-2699.2011.02523.x · DOI
  195. Nenzén, 2011, Choice of threshold alters projections of species range shifts under climate change, Ecol. Modell., 222, 3346, 10.1016/j.ecolmodel.2011.07.011 · DOI
  196. New, 2002, A high-resolution data set of surface climate over global land areas, Clim. Res., 1, 1, 10.3354/cr021001 · DOI
  197. Niamir, 2016, Use of taxonomy to delineate spatial extent of atlas data for species distribution models, Glob. Ecol. Biogeogr., 25, 227, 10.1111/geb.12405 · DOI
  198. Nogués-Bravo, 2008, Climate change, humans, and the extinction of the woolly mammoth, PLoS Biol, 4, e79, 10.1371/journal.pbio.0060079 · DOI
  199. Nogués Bravo, 2009, Predicting the past distribution of species climatic niches, Glob. Ecol. Biogeogr., 5, 521, 10.1111/j.1466-8238.2009.00476.x · DOI
  200. Olivera-Gomez, 2005, Distribution of the antillean manatee (trichechus manatus manatus) as a function of habitat characteristics, in bahia de chetumal, mexico, Biol. Conserv., 1, 127, 10.1016/j.biocon.2004.02.023 · DOI

200 of 295 shown

Cited by

Sorted by citations

See all 456

Metadata

Authors
Sillero, N., Arenas-Castro, S., Enriquez‐Urzelai, U., Vale, C. G., Sousa-Guedes, D., Martínez-Freiría, F., Real, R., & Barbosa, A. M.
Venue
Ecological Modelling
Publisher
Elsevier BV
Published
2021-09
Volume
456
Pages
109671
ISSN
0304-3800
DOI
10.1016/j.ecolmodel.2021.109671
Cited by
456
References
8
Language
en
Licence
Funders
Fundação para a Ciência e a Tecnologia (CEECIND/02213/2017, POCI-01\u20130145-FEDER-022127, DL57/2016/CP1440/CT0010)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review