CoChat
Sign Up
Cited by 406

The DOI matches its Crossref record, with a small difference in title or year; 5 of the 5 references it cites were found in a registry or an index.

Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions

Wilson, Adam M., Jetz, Walter·2016·PLOS Biology
DOI 10.1371/journal.pbio.1002415PMC PMC4816575
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pbio.1002415

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Mean annual cloud frequency and precipitation of the driest quarter were factors that influence the formation of low-lying, characteristic clouds of the Andean humid montane forests.

    “Mean annual cloud frequency and precipitation of the driest quarter were factors that influence the formation of low-lying, characteristic clouds of the Andean humid montane forests [65,66].”

    From Potential distributions of pre-columbian people in tropical andean landscapes (2022) · cites this paper as [66]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 61

  1. C Perrings, 2010, Ecosystem services for 2020, Science(Washington), 330, 323, 10.1126/science.1196431 · DOI
  2. HM Pereira, 2013, Essential Biodiversity Variables, Science, 339, 277, 10.1126/science.1229931 · DOI · Paper page
  3. A Larigauderie, 2010, The Intergovernmental science-policy Platform on Biodiversity and Ecosystem Services: moving a step closer to an IPCC-like mechanism for biodiversity, Current Opinion in Environmental Sustainability, 2, 9, 10.1016/j.cosust.2010.02.006 · DOI
  4. KA Potter, 2013, Microclimatic challenges in global change biology, Global Change Biology, 19, 2932, 10.1111/gcb.12257 · DOI
  5. RW Soria-Auza, 2010, Impact of the quality of climate models for modelling species occurrences in countries with poor climatic documentation: a case study from Bolivia, Ecological Modelling, 221, 1221, 10.1016/j.ecolmodel.2010.01.004 · DOI
  6. C Storlie, 2014, Stepping inside the niche: microclimate data are critical for accurate assessment of species’ vulnerability to climate change, Biol Lett, 10, 20140576, 10.1098/rsbl.2014.0576 · DOI
  7. RJ Hijmans, 2005, Others. Very high resolution interpolated climate surfaces for global land areas, International Journal of Climatology, 25, 1965, 10.1002/joc.1276 · DOI
  8. RA Houze, 2012, Orographic effects on precipitating clouds, Reviews of Geophysics, 50, 10.1029/2011RG000365 · DOI
  9. AM Wilson, 2014, Estimating uncertainty in daily weather interpolations: a Bayesian framework for developing climate surfaces, Int J Climatol, 34, 2573, 10.1002/joc.3859 · DOI
  10. KM Hare, 2010, Exploring the consequences of climate-induced changes in cloud cover on offspring of a cool-temperate viviparous lizard, Biological Journal of the Linnean Society, 101, 844, 10.1111/j.1095-8312.2010.01536.x · DOI
  11. GR Goldsmith, 2013, The incidence and implications of clouds for cloud forest plant water relations, Ecology Letters, 16, 307, 10.1111/ele.12039 · DOI
  12. EA Graham, 2003, Cloud cover limits net CO2 uptake and growth of a rainforest tree during tropical rainy seasons, PNAS, 100, 572, 10.1073/pnas.0133045100 · DOI
  13. P Sklenář, 2008, Cloud frequency correlates to plant species composition in the high Andes of Ecuador, Basic and Applied Ecology, 9, 504, 10.1016/j.baae.2007.09.007 · DOI
  14. SJ Wright, 1994, Light and the Phenology of Tropical Trees, The American Naturalist, 143, 192, 10.1086/285600 · DOI
  15. DC Morton, 2014, Amazon forests maintain consistent canopy structure and greenness during the dry season, Nature
  16. DT Fischer, 2009, Significance of summer fog and overcast for drought stress and ecological functioning of coastal California endemic plant species, Journal of Biogeography, 36, 783, 10.1111/j.1365-2699.2008.02025.x · DOI
  17. C. J Stubenrauch, 2013, Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX Radiation Panel, Bulletin of the American Meteorological Society
  18. D Wylie, 2005, Trends in global cloud cover in two decades of HIRS observations, Journal of climate, 18, 3021, 10.1175/JCLI3461.1 · DOI
  19. MJ Foster, 2012, PATMOS-x: Results from a Diurnally Corrected 30-yr Satellite Cloud Climatology, J Climate, 26, 414, 10.1175/JCLI-D-11-00666.1 · DOI
  20. KR Knapp, 2011, Globally gridded satellite observations for climate studies, Bulletin of the American Meteorological Society, 92, 893, 10.1175/2011BAMS3039.1 · DOI
  21. AZ Kotarba, 2015, Regional high-resolution cloud climatology based on MODIS cloud detection data, Int J Climatol
  22. AM Wilson, 2014, Systematic land cover bias in Collection 5 MODIS cloud mask and derived products—A global overview, Remote Sensing of Environment, 141, 149, 10.1016/j.rse.2013.10.025 · DOI
  23. Douglas M. A high spatial resolution satellite-based cloud climatology for biogeographical applications. 6th International Conference. Miami, FL: International Biogeography Society; 2013. http://www.nssl.noaa.gov/projects/pacs/web/MODIS/
  24. J Descloitres, 2002, The MODIS rapid response project, Geoscience and Remote Sensing Symposium, 2002 IGARSS ‘02 2002 IEEE International, vol.2, 1191, 10.1109/IGARSS.2002.1025879 · DOI
  25. J Fjeldsaå, 1997, Are biodiversity “hotspots” correlated with current ecoclimatic stability? A pilot study using the NOAA-AVHRR remote sensing data, Biodiversity and Conservation, 6, 401, 10.1023/A:1018364708207 · DOI
  26. W Jetz, 2004, The coincidence of rarity and richness and the potential signature of history in centres of endemism, Ecology Letters, 7, 1180, 10.1111/j.1461-0248.2004.00678.x · DOI
  27. B Sandel, 2011, The Influence of Late Quaternary Climate-Change Velocity on Species Endemism, Science, 334, 660, 10.1126/science.1210173 · DOI
  28. J Allard, 2010, Mesoscale Associations Between Midwest Land Surface Properties and Convective Cloud Development in the Warm Season, Physical Geography, 31, 107, 10.2747/0272-3646.31.2.107 · DOI
  29. L Durieux, 2003, The impact of deforestation on cloud cover over the Amazon arc of deforestation, Remote Sensing of Environment, 86, 132, 10.1016/S0034-4257(03)00095-6 · DOI
  30. J Wang, 2009, Impact of deforestation in the Amazon basin on cloud climatology, PNAS, 106, 3670, 10.1073/pnas.0810156106 · DOI
  31. CG Markham, 1970, Seasonality of Precipitation in the United States, Annals of the Association of American Geographers, 60, 593, 10.1111/j.1467-8306.1970.tb00743.x · DOI
  32. R Ponce-Reyes, 2012, Vulnerability of cloud forest reserves in Mexico to climate change, Nature Clim Change, 2, 448, 10.1038/nclimate1453 · DOI
  33. US Nair, 2008, Biogeography of Tropical Montane Cloud Forests. Part II: Mapping of Orographic Cloud Immersion, Journal of Applied Meteorology and Climatology, 47, 2183, 10.1175/2007JAMC1819.1 · DOI
  34. AH Gentry, 1992, Tropical Forest Biodiversity: Distributional Patterns and Their Conservational Significance, Oikos, 63, 19, 10.2307/3545512 · DOI
  35. CJ Still, 1999, Simulating the effects of climate change on tropical montane cloud forests, Nature, 398, 608, 10.1038/19293 · DOI
  36. V Wulfmeyer, 2014, The Impact of Plantations on Weather and Climate in Coastal Desert Regions, J Appl Meteor Climatol, 53, 1143, 10.1175/JAMC-D-13-0208.1 · DOI
  37. R Mahmood, 2014, Land cover changes and their biogeophysical effects on climate, Int J Climatol, 34, 929, 10.1002/joc.3736 · DOI
  38. AJ Pitman, 2004, Impact of land cover change on the climate of southwest Western Australia, J Geophys Res, 109, D18109, 10.1029/2003JD004347 · DOI
  39. W Fithian, 2013, Finite-sample equivalence in statistical models for presence-only data, Ann Appl Stat, 7, 1917, 10.1214/13-AOAS667 · DOI
  40. AF Cord, 2013, Modelling species distributions with remote sensing data: bridging disciplinary perspectives, J Biogeogr, 40, 2226, 10.1111/jbi.12199 · DOI
  41. JT Kerr, 2007, The Macroecological Contribution to Global Change Solutions, Science, 316, 1581, 10.1126/science.1133267 · DOI
  42. W Jetz, 2012, Integrating biodiversity distribution knowledge: toward a global map of life, Trends in Ecology & Evolution, 27, 151, 10.1016/j.tree.2011.09.007 · DOI
  43. TV Ramachandra, 2011, Hotspots of solar potential in India, Renewable and Sustainable Energy Reviews, 15, 3178, 10.1016/j.rser.2011.04.007 · DOI
  44. FJ Tapiador, 2009, Assessment of renewable energy potential through satellite data and numerical models, Energy Environ Sci, 2, 1142, 10.1039/b914121a · DOI
  45. M Rutty, 2013, Differential climate preferences of international beach tourists, Clim Res, 57, 259, 10.3354/cr01183 · DOI
  46. F Petitcolin, 2002, Land surface reflectance, emissivity and temperature from MODIS middle and thermal infrared data, Remote Sensing of Environment, 83, 112, 10.1016/S0034-4257(02)00094-9 · DOI
  47. JC Roger, 1998, A Method to Retrieve the Reflectivity Signature at 3.75 μm from AVHRR Data, Remote Sensing of Environment, 64, 103, 10.1016/S0034-4257(97)00173-9 · DOI
  48. E Kalnay, 1996, The NCEP/NCAR 40-Year Reanalysis Project, Bulletin of the American Meteorological Society, 77, 437, 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2 · DOI
  49. R Liu, 2013, Generation of new cloud masks from MODIS land surface reflectance products, Remote Sensing of Environment, 133, 21, 10.1016/j.rse.2013.01.019 · DOI
  50. AK Heidinger, 2012, A naive Bayesian cloud-detection scheme derived from CALIPSO and applied within PATMOS-x, Journal of Applied Meteorology and Climatology, 51, 1129, 10.1175/JAMC-D-11-02.1 · DOI
  51. WW Gregg, 2007, Sampling biases in MODIS and SeaWiFS ocean chlorophyll data, Remote Sensing of Environment, 111, 25, 10.1016/j.rse.2007.03.008 · DOI
  52. J Fehrenbach, 2012, Variational Algorithms to Remove Stationary Noise: Applications to Microscopy Imaging, IEEE Transactions on Image Processing, 21, 4420, 10.1109/TIP.2012.2206037 · DOI
  53. A Dybbroe, 2005, NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part II: Tuning and Validation, Journal of Applied Meteorology, 44, 55, 10.1175/JAM-2189.1 · DOI
  54. Y Yang, 2007, Selection of the automated thresholding algorithm for the Multi-angle Imaging SpectroRadiometer Radiometric Camera-by-Camera Cloud Mask over land, Remote Sensing of Environment, 107, 159, 10.1016/j.rse.2006.05.020 · DOI
  55. SA Ackerman, 1998, Discriminating clear sky from clouds with MODIS, Journal of Geophysical Research: Atmospheres, 103, 32141, 10.1029/1998JD200032 · DOI
  56. BA Raut, 2009, Spatial distribution and diurnal variation of cumuliform clouds during Indian Summer Monsoon, J Geophys Res, 114, D11208, 10.1029/2008JD011153 · DOI
  57. SA Ackerman, 2013, Satellite Regional Cloud Climatology over the Great Lakes, Remote Sensing, 5, 6223, 10.3390/rs5126223 · DOI
  58. J Elith, 2011, A statistical explanation of MaxEnt for ecologists, Diversity and Distributions, 17, 43, 10.1111/j.1472-4642.2010.00725.x · DOI · Paper page
  59. BL Sullivan, 2014, The eBird enterprise: An integrated approach to development and application of citizen science, Biological Conservation, 169, 31, 10.1016/j.biocon.2013.11.003 · DOI
  60. AM Latimer, 2006, Building Statistical Models To Analyze Species Distributions, Ecological Applications, 16, 33, 10.1890/04-0609 · DOI
  61. G Vieilledent, 2014, hSDM: hierarchical Bayesian species distribution models [Internet], CRAN

Cited by

Sorted by citations

Metadata

Authors
Wilson, A. M., & Jetz, W.
Venue
PLOS Biology
Publisher
Public Library of Science (PLoS)
Published
2016-03-31
Volume
14
Issue
3
Pages
e1002415
ISSN
1545-7885
DOI
10.1371/journal.pbio.1002415
PMC
PMC4816575
Cited by
406
References
5
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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