Search

Menu
Search
in Ecology

National human footprint maps for Peru and Ecuador

36 Views


Abstract

Human Footprint (HF) maps score human pressures based on their influence and integrate them into a single spatial index to assess the naturalness of ecosystems. We produced a historical series of national HF maps for Peru and Ecuador for Sustainable Development Goal 15 (SDG15) reporting. These maps integrate pressures from built environments, land use/land cover (LULC—agriculture, pasture, tree plantations), roads and railways, population density, electrical infrastructure, oil and gas infrastructure, and mining. The dataset includes HF maps and individual pressure maps for Peru from 2012 to 2021, as well as for Ecuador for the years 2014, 2016, 2018, 2020, and 2022. These maps support the analysis of spatiotemporal patterns of human influence at national and subnational levels, enabling biodiversity monitoring, modelling, and conservation in these highly biodiverse countries.

Similar content being viewed by others

A global record of annual terrestrial Human Footprint dataset from 2000 to 2018

Article
Open access
19 April 2022

Human expansion into Asian highlands in the 21st Century and its effects

Article
Open access
24 August 2022

Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858–2020

Article
Open access
31 May 2022

Data availability

The full dataset is available at https://doi.org/10.6084/m9.figshare.30226402.

Code availability

The Python code is available for download on GitHub (https://github.com/joluaros/National_Human_Footprint_maps). Note that the scripts require a pre-assembled input database. Users can access the necessary datasets via the links provided in the methods or submit requests where applicable. For guidance on installing the Anaconda environment and executing the scripts, please refer to the Readme file.

References

  1. United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development. https://sdgs.un.org/2030agenda (2015).

  2. United Nations. Work of the Statistical Commission Pertaining to the 2030 Agenda for Sustainable Development A/RES/71/313. https://undocs.org/A/RES/71/313 (2017).

  3. IAEG-SDGs. The SDGs Geospatial Roadmap. https://mdgs.un.org/unsd/statcom/53rd-session/documents/BG-3a-SDGs-Geospatial-Roadmap-E.pdf (2022).

  4. United Nations. SDG Indicators. https://unstats.un.org/sdgs/indicators/indicators-list/ (2017).

  5. Hansen, A. J. et al. Developing national complementary indicators of SDG15 that consider forest quality: Applications in Colombia, Ecuador, and Peru. Ecol Indic 159, 111654 (2024).

    Google Scholar 

  6. Williams, B. A. et al. Change in Terrestrial Human Footprint Drives Continued Loss of Intact Ecosystems. One Earth 3, 371–382 (2020).

    Google Scholar 

  7. Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci Data 3, 160067 (2016).

    Google Scholar 

  8. Sanderson, E. W. et al. The Human Footprint and the Last of the Wild. Bioscience 52, 891 (2002).

    Google Scholar 

  9. Aragon-Osejo, J. et al. The TARA framework for developing SDG indicator data: applications in Human Footprint Mapping in Peru and Ecuador. Preprint at https://doi.org/10.21203/rs.3.rs-6186053/v1 (2025).

  10. ESRI. World Imagery. https://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9 (2021).

  11. United Nations. Goal 15 Life on Land. https://www.un.org/sustainabledevelopment/biodiversity/ (2015).

  12. Watson, J. E. M. M. et al. The exceptional value of intact forest ecosystems. Nat Ecol Evol 2, 599–610 (2018).

    Google Scholar 

  13. Potapov, P. et al. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Sci Adv 3 (2017).

  14. Veneros, J., Hansen, A. J., Jantz, P., Noguera-Urbano, E. & García, L. Forecasting the potential habitat for the spectacled bear and the Páramo ecoregion for current conditions and climate change scenarios in 2050: A contribution to SDG 15 in Perú, Ecuador and Colombia. Environmental and Sustainability Indicators 26, 100639 (2025).

    Google Scholar 

  15. Ramos, D. et al. Rainforest conversion to rubber and oil palm reduces abundance, biomass and diversity of canopy spiders. PeerJ 10, e13898 (2022).

    Google Scholar 

  16. Halpern, B. S. & Fujita, R. Assumptions, challenges, and future directions in cumulative impact analysis. Ecosphere 4, art131 (2013).

    Google Scholar 

  17. Riggio, J. et al. Global human influence maps reveal clear opportunities in conserving Earth’s remaining intact terrestrial ecosystems. Glob Chang Biol 26, 4344–4356 (2020).

    Google Scholar 

  18. Watson, J. E. M. et al. Catastrophic Declines in Wilderness Areas Undermine Global Environment Targets. Current Biology 26, 2929–2934 (2016).

    Google Scholar 

  19. Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. M. Changes in human footprint drive changes in species extinction risk. Nat Commun 9, 4621 (2018).

    Google Scholar 

  20. Pillay, R. et al. Global rarity of high-integrity tropical rainforests for threatened and declining terrestrial vertebrates. Proceedings of the National Academy of Sciences 121 (2024).

  21. Ramírez-Delgado, J. P. et al. Matrix condition mediates the effects of habitat fragmentation on species extinction risk. Nat Commun 13, 595 (2022).

    Google Scholar 

  22. Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science (1979) 360, 788–791 (2018).

    Google Scholar 

  23. Pärtel, M. et al. Global impoverishment of natural vegetation revealed by dark diversity. Nature 1–8, https://doi.org/10.1038/s41586-025-08814-5 (2025).

  24. Marjakangas, E., Johnston, A., Santangeli, A. & Lehikoinen, A. Bird species’ tolerance to human pressures and associations with population change. Global Ecology and Biogeography 33, e13816 (2024).

    Google Scholar 

  25. Skinner, E. B., Glidden, C. K., MacDonald, A. J. & Mordecai, E. A. Human footprint is associated with shifts in the assemblages of major vector-borne diseases. Nat Sustain 1–10, https://doi.org/10.1038/s41893-023-01080-1 (2023).

  26. Villalobos-Segura, M. D. C., Rico-Chávez, O., Suzán, G. & Chaves, A. Influence of Host and Landscape-Associated Factors in the Infection and Transmission of Pathogens: The Case of Directly Transmitted Virus in Mammals. Vet Med Sci 11 (2025).

  27. Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat Commun 11, 5978 (2020).

    Google Scholar 

  28. Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F.-C. & Taneja, J. Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019. Remote Sens (Basel) 13, 922 (2021).

    Google Scholar 

  29. MAE-MAGAP. Mapa de cobertura y uso de la tierra del Ecuador continental, escala 1:100.000. http://ide.ambiente.gob.ec/mapainteractivo/ (2015).

  30. MAE-MAGAP. Protocolo metodológico para la elaboración del Mapa de cobertura y uso de la tierra del Ecuador continental 2013 – 2014, escala 1:100.000. 1–49 (2015).

  31. Proyecto MapBiomas Perú. Colección 1 de la Serie Anual de Mapas de Cobertura y Uso del Suelo del Perú. https://peru.mapbiomas.org/ (2023).

  32. Anaconda Inc. Anaconda. https://www.anaconda.com/ (2016).

  33. Gennari, P. & Navarro, D. K. Validation of methods and data for SDG indicators. Stat J IAOS 35, 735–741 (2019).

    Google Scholar 

  34. Correa Ayram, C. A. et al. Spatiotemporal evaluation of the human footprint in Colombia: Four decades of anthropic impact in highly biodiverse ecosystems. Ecol Indic 117, 106630 (2020).

    Google Scholar 

  35. Tapia-Armijos, M. F., Homeier, J. & Draper Munt, D. Spatio-temporal analysis of the human footprint in South Ecuador: Influence of human pressure on ecosystems and effectiveness of protected areas. Applied Geography 78, 22–32 (2017).

    Google Scholar 

  36. Hua, T., Zhao, W., Cherubini, F., Hu, X. & Pereira, P. Continuous growth of human footprint risks compromising the benefits of protected areas on the Qinghai-Tibet Plateau. Glob. Ecol Conserv 34, e02053 (2022).

    Google Scholar 

  37. Zhao, Y. & Zhu, Z. ASI: An artificial surface Index for Landsat 8 imagery. International Journal of Applied Earth Observation and Geoinformation 107, 102703 (2022).

    Google Scholar 

  38. Zheng, K., He, G., Yin, R., Wang, G. & Long, T. A Comparison of Seven Medium Resolution Impervious Surface Products on the Qinghai–Tibet Plateau, China from a User’s Perspective. Remote Sens (Basel) 15, 2366 (2023).

  39. Instituto Geográfico Militar. Base escala 1:50.000. http://www.geoportaligm.gob.ec/portal/index.php/cartografia-de-libre-acceso-escala-50k/ (2011).

  40. Contributors ©OpenStreetMap. OpenStreetMap. https://www.openstreetmap.org/about (2013).

  41. Davidovic, N., Mooney, P. & Stoimenov, L. An analysis of tagging practices and patterns in urban areas in OpenStreetMap. agile-online.org https://agile-online.org/Conference_Paper/cds/agile_2016/shortpapers/135_Paper_in_PDF.pdf (2016).

  42. Touya, G. & Reimer, A. Inferring the Scale of OpenStreetMap Features. in 81–99, https://doi.org/10.1007/978-3-319-14280-7_5 (2015).

  43. Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch‐Mordo, S. & Kiesecker, J. Managing the middle: A shift in conservation priorities based on the global human modification gradient. Glob Chang Biol 25, 811–826 (2019).

    Google Scholar 

  44. Gassert, F. et al. An operational approach to near real time global high resolution mapping of the terrestrial Human Footprint. Frontiers in Remote Sensing 4 (2023).

  45. Gaona, F. P., Iñiguez-Armijos, C., Brehm, G., Fiedler, K. & Espinosa, C. I. Drastic loss of insects (Lepidoptera: Geometridae) in urban landscapes in a tropical biodiversity hotspot. J Insect Conserv 25, 395–405 (2021).

    Google Scholar 

  46. Aronson, M. F. J. et al. A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proceedings of the Royal Society B: Biological Sciences 281, 20133330 (2014).

    Google Scholar 

  47. Møller, A. P. Environmental Indicators of Biological Urbanization. in Environmental Indicators 421–432, https://doi.org/10.1007/978-94-017-9499-2_25 (Springer Netherlands, Dordrecht, 2015).

  48. Facebook Connectivity Lab, CIESIN & DigitalGlobe. High Resolution Settlement Layer (HRSL). https://data.humdata.org/dataset/ecuador-high-resolution-population-density-maps-demographic-estimates (2016).

  49. Ministerio del Ambiente de Perú. Mapa de Vulnerabilidad Física del Perú. https://geoservidor.minam.gob.pe/recursos/intercambio-de-datos/ (2011).

  50. Ministerio del Ambiente de Perú. Mapa Nacional de Cobertura Vegetal (Memoria descriptiva). https://www.gob.pe/institucion/minam/informes-publicaciones/2674-mapa-nacional-de-cobertura-vegetal-memoria-descriptiva (2015).

  51. Ministerio de Cultura de Perú. Base de Datos Oficial de Pueblos Indígenas u Originarios [by request]. bdpi.cultura.gob.pe (2020).

  52. Ortega-Andrade, H. M. et al. Red List assessment of amphibian species of Ecuador: A multidimensional approach for their conservation. PLoS One 16, e0251027 (2021).

    Google Scholar 

  53. Webb, J. et al. Mercury in Fish-eating Communities of the Andean Amazon, Napo River Valley, Ecuador. Ecohealth 1, SU59–SU71 (2004).

    Google Scholar 

  54. Ross, C., Fildes, S. & Millington, A. Land-Use and Land-Cover Change in the Páramo of South‐Central Ecuador, 1979–2014. Land (Basel) 6, 46 (2017).

    Google Scholar 

  55. Kleemann, J. et al. Deforestation in Continental Ecuador with a Focus on Protected Areas. Land (Basel) 11, 268 (2022).

    Google Scholar 

  56. Rojas, E., Zutta, B. R., Velazco, Y. K., Montoya-Zumaeta, J. G. & Salvà-Catarineu, M. Deforestation risk in the Peruvian Amazon basin. Environ Conserv 48, 310–319 (2021).

    Google Scholar 

  57. Marin, N. A. et al. Spatiotemporal Dynamics of Grasslands Using Landsat Data in Livestock Micro-Watersheds in Amazonas (NW Peru). Land (Basel) 11, 674 (2022).

    Google Scholar 

  58. Chávez Michaelsen, A. et al. Regional Deforestation Trends within Local Realities: Land-Cover Change in Southeastern Peru 1996–2011. Land (Basel) 2, 131–157 (2013).

    Google Scholar 

  59. Heredia-R, M. et al. Land Use and Land Cover Changes in the Diversity and Life Zone for Uncontacted Indigenous People: Deforestation Hotspots in the Yasuní Biosphere Reserve, Ecuadorian Amazon. https://doi.org/10.3390/f12111539 (2021).

  60. López-Bedoya, P. A., Cardona-Galvis, E. A., Urbina-Cardona, J. N., Edwards, F. A. & Edwards, D. P. Impacts of pastures and forestry plantations on herpetofauna: A global meta-analysis. Journal of Applied Ecology 59, 3038–3048 (2022).

    Google Scholar 

  61. Ministerio de Desarrollo Agrario y Riego de Perú. Mapa Nacional de Superficie Agrícola de Perú. Inactive link: https://archivos.minagri.gob.pe/index.php/s/deZQjkzmbodFyG6/download (2018).

  62. Ministerio de Agricultura y Ganadería de Ecuador. Plantación Forestal. http://geoportal.agricultura.gob.ec/geonetwork/srv/spa/catalog.search;jsessionid=6890EE0A879CA39C1D519225321EF283#/metadata/427ef898-a404-4eba-8cc7-800a90f1eb37 (2017).

  63. Buzzelli, M. Modifiable Areal Unit Problem. in International Encyclopedia of Human Geography 169–173, https://doi.org/10.1016/B978-0-08-102295-5.10406-8 (Elsevier, 2020).

  64. Freire S., MacManus K., Pesaresi M., Doxsey-Whitfield E., M. J. Development of new open and free multi-temporal global population grids at 250 m resolution. Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE). https://ghsl.jrc.ec.europa.eu/ghs_pop2023.php, https://doi.org/10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE (2016).

  65. Lloyd, C. T. et al. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data 3 (2019).

  66. Stevens, F. R., Gaughan, A. E., Linard, C. & Tatem, A. J. Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS One 10, e0107042 (2015).

    Google Scholar 

  67. WorldPop & CIESIN. Global High Resolution Population Denominators Project – Funded by The Bill and Melinda Gates Foundation (OPP1134076). www.worldpop.org, https://doi.org/10.5258/SOTON/WP00674 (2018).

  68. Instituto Nacional de Estadística e Informática de Perú. Censo Nacional de Población. https://censo2017.inei.gob.pe/ (2017).

  69. Instituto Nacional de Estadística y Censos de Ecuador. Population and Housing Census 2010. http://www.ecuadorencifras.gob.ec/base-de-datos-censo-de-poblacion-y-vivienda-2010/ (2010).

  70. Suárez, E., Zapata-Ríos, G., Utreras, V., Strindberg, S. & Vargas, J. Controlling access to oil roads protects forest cover, but not wildlife communities: a case study from the rainforest of Yasuní Biosphere Reserve (Ecuador). Anim Conserv 16, 265–274 (2013).

    Google Scholar 

  71. Coomes, O. T., Abizaid, C. & Lapointe, M. Human modification of a large meandering Amazonian river: genesis, ecological and economic consequences of the Masisea cutoff on the central Ucayali, Peru. AMBIO: A. Journal of the Human Environment 38, 130–134 (2009).

    Google Scholar 

  72. Nunez-Iturri, G., Olsson, O. & Howe, H. F. Hunting reduces recruitment of primate-dispersed trees in Amazonian Peru. Biol Conserv 141, 1536–1546 (2008).

    Google Scholar 

  73. Sierra, R., Tirado, M. & Palacios, W. Forest-cover change from labor- and capital-intensive commercial logging in the southern Chocó rainforests. Professional Geographer 55, 477–490 (2003).

    Google Scholar 

  74. Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018).

    Google Scholar 

  75. Watmough, G. R. et al. Using open-source data to construct 20 metre resolution maps of children’s travel time to the nearest health facility. Sci Data 9, 217 (2022).

    Google Scholar 

  76. Sierra, R. et al. Territorial Implications of Economic Diversification in the Waorani Ancestral Lands. in 57–80, https://doi.org/10.1007/978-3-031-22680-9_4 (2023).

  77. Banerjee, A., Ye, X. & Pendyala, R. M. Understanding Travel Time Expenditures Around the World: Exploring the Notion of a Travel Time Frontier. Transportation (Amst) 34, 51–65 (2007).

    Google Scholar 

  78. Weiss, D. J. et al. Global maps of travel time to healthcare facilities. Nat Med 26, 1835–1838 (2020).

    Google Scholar 

  79. Baynard, C. W., Ellis, J. M. & Davis, H. Roads, petroleum and accessibility: the case of eastern Ecuador. GeoJournal 78, 675–695 (2013).

    Google Scholar 

  80. Schielein, J. et al. The role of accessibility for land use and land cover change in the Brazilian Amazon. Applied Geography 132, 102419 (2021).

    Google Scholar 

  81. Delamater, P. L., Messina, J. P., Shortridge, A. M. & Grady, S. C. Measuring geographic access to health care: raster and network-based methods. Int J Health Geogr 11, 15 (2012).

    Google Scholar 

  82. Pinto, F. A. S., Clevenger, A. P. & Grilo, C. Effects of roads on terrestrial vertebrate species in Latin America. Environ Impact Assess Rev 81, 106337 (2020).

    Google Scholar 

  83. Vasco, C., Tamayo, G. & Griess, V. The Drivers of Market Integration Among Indigenous Peoples: Evidence From the Ecuadorian Amazon. Soc Nat Resour 30, 1212–1228 (2017).

    Google Scholar 

  84. Filius, J., Hoek, Y., Jarrín‐V, P. & Hooft, P. Wildlife roadkill patterns in a fragmented landscape of the Western Amazon. Ecol Evol 10, 6623–6635 (2020).

    Google Scholar 

  85. Guzmán, P., Benítez, Á., Carrión-Paladines, V., Salinas, P. & Cumbicus, N. Elevation and Soil Properties Determine Community Composition, but Not Vascular Plant Richness in Tropical Andean Roadside. Forests 13, 685 (2022).

    Google Scholar 

  86. Dorsey, B., Olsson, M. & Rew, L. J. Ecological Effects of Railways on Wildlife. in Handbook of Road Ecology 219–227, https://doi.org/10.1002/9781118568170.ch26 (Wiley, 2015).

  87. Ministerio de Transportes y Comunicaciones. Sistema Nacional de Carreteras. Inactive link: http://mtcgeo2.mtc.gob.pe/MTC_pub/intercambio/shapefiles/ (2019).

  88. Li, X., Elvidge, C., Zhou, Y., Cao, C. & Warner, T. Remote sensing of night-time light. Int J Remote Sens 38, 5855–5859 (2017).

    Google Scholar 

  89. Castro, M., Sierra, R., Calva, O., Camacho, J. & López, F. Zonas de Procesos Homogéneos de Deforestación Del Ecuador: Factores Promotores y Tendencias al 2020. https://www.researchgate.net/publication/268390247_Zonas_de_Procesos_Homogeneos_de_Deforestacion_del_Ecuador_Factores_promotores_y_tendencias_al_2020 (2013).

  90. Bruederle, A. & Hodler, R. Nighttime lights as a proxy for human development at the local level. PLoS One 13, e0202231 (2018).

    Google Scholar 

  91. Sierra, R. Patrones y Factores de Deforestación En El Ecuador Continental, 1990–2010. Y Un Acercamiento Los Próximos 10 Años. http://www.forest-trends.org/documents/files/doc_3396.pdf (2013).

  92. Zhao et al. Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives. Remote Sens (Basel) 11, 1971 (2019).

    Google Scholar 

  93. Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C. & Ghosh, T. VIIRS night-time lights. Int J Remote Sens 38, 5860–5879 (2017).

    Google Scholar 

  94. Agencia de Regulación y Control de Energía y Recursos Naturales no Renovables. Infraestructura eléctrica [by request] (2019).

  95. Organismo Supervisor de la Inversión en Energía y Minería. Infraestructura petrolera [by request] (2018).

  96. Llerena-Montoya, S. et al. Multitemporal Analysis of Land Use and Land Cover within an Oil Block in the Ecuadorian Amazon. ISPRS Int J Geoinf 10, 191 (2021).

    Google Scholar 

  97. Rivera-Parra, J. L., Vizcarra, C., Mora, K., Mayorga, H. & Dueñas, J. C. Spatial distribution of oil spills in the north eastern Ecuadorian Amazon: A comprehensive review of possible threats. Biol Conserv 252, 108820 (2020).

    Google Scholar 

  98. Herrera-Franco, G., Escandón-Panchana, P., Erazo, K., Mora-Frank, C. & Berrezueta, E. Geoenvironmental analysis of oil extraction activities in urban and rural zones of Santa Elena Province. Ecuador. International Journal of Energy Production and Management 6, 211–228 (2021).

    Google Scholar 

  99. Agbagwa, I. O. & Ndukwu, B. C. Oil and Gas Pipeline Construction-Induced Forest Fragmentation and Biodiversity Loss in the Niger Delta. Nigeria. Natural Resources 05, 698–718 (2014).

    Google Scholar 

  100. PERUPETRO. Pozos petroleros. https://perupetro.maps.arcgis.com/apps/webappviewer/index.html?id=6a830a470b934f0687c8ed84c2bacacc (2019).

  101. Ministerio de Energía y Minas. Infraestructura Sector Hidrocarburos [by request] (2020).

  102. V Capparelli, M. et al. An Integrative Approach to Assess the Environmental Impacts of Gold Mining Contamination in the Amazon. Toxics 9, 149 (2021).

    Google Scholar 

  103. Sánchez-Cuervo, A. M. et al. Twenty years of land cover change in the southeastern Peruvian Amazon: implications for biodiversity conservation. Reg Environ Change 20, 8 (2020).

    Google Scholar 

  104. Bebbington, A. & Williams, M. Water and Mining Conflicts in Peru. Mt Res Dev 28, 190–195 (2008).

    Google Scholar 

  105. Markham, K. E. & Sangermano, F. Evaluating Wildlife Vulnerability to Mercury Pollution From Artisanal and Small-Scale Gold Mining in Madre de Dios, Peru. Trop Conserv Sci 11, 194008291879432 (2018).

    Google Scholar 

  106. Alvarez-Berríos, N. et al. Impacts of Small-Scale Gold Mining on Birds and Anurans Near the Tambopata Natural Reserve, Peru, Assessed Using Passive Acoustic Monitoring. Trop Conserv Sci 9, 832–851 (2016).

    Google Scholar 

  107. Mestanza-Ramón, C. et al. Gold Mining in the Amazon Region of Ecuador: History and a Review of Its Socio-Environmental Impacts. Land (Basel) 11, 221 (2022).

    Google Scholar 

  108. Maus, V. et al. A global-scale data set of mining areas. Sci Data 7, 289 (2020).

    Google Scholar 

  109. Maus, V. & Werner, T. T. Impacts for half of the world’s mining areas are undocumented. Nature 2024 625:7993 625, 26–29 (2024).

    Google Scholar 

  110. Aragon-Osejo, J. et al. Peru and Ecuador Human Footprint Maps. https://doi.org/10.6084/m9.figshare.30226402 (2025).

  111. Llano, X. AcATaMa – QGIS plugin for Accuracy Assessment of Thematic Maps, v19.11.21. https://plugins.qgis.org/plugins/AcATaMa (2019).

  112. Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens Environ 148, 42–57 (2014).

    Google Scholar 

  113. European Space Agency. Land Cover CCI Product User Guide V. 2.0. maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (2017).

  114. Dwiyahreni, A. A. et al. Changes in the human footprint in and around Indonesia’s terrestrial national parks between 2012 and 2017. Sci Rep 11, 4510 (2021).

  115. Mu, H. et al. A global record of annual terrestrial Human Footprint dataset from 2000 to 2018. Sci Data 9, 176 (2022).

    Google Scholar 

  116. Lozano, J. et al. Diversity and biogeographical patterns in the diet of the culpeo in South America. Ecol Evol 14 (2024).

  117. Tucker, M. A. et al. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science (1979) 359, 466–469 (2018).

    Google Scholar 

  118. Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat Commun 7, 12558 (2016).

    Google Scholar 

Download references

Acknowledgements

The NASA Biodiversity and Ecological Forecasting Program funded the work under the 2016 A.8 Sustaining Living Systems in a Time of Climate Variability and Change solicitation under Grant number 80NSSC19K0186. We want to thank everyone at the Life on Land project for all their work. We thank the following individuals for their valuable support in this study: Mauricio Trujillo, Maria Olga Borja, Rodrigo Torres, Cícero Augusto, Gonzalo Morales, and everyone at the Conservation Solutions Lab and the GIS Lab at UNBC. ChatGPT was used for language suggestions.

Author information

Authors and Affiliations

  1. Natural Resources and Environmental Studies Institute, University of Northern British Columbia, Prince George, BC, Canada

    Jose Aragon-Osejo & Oscar Venter

  2. Ministerio del Ambiente, Agua y Transición Ecológica de Ecuador (MAATE), Quito, Ecuador

    Lenin Beltrán, Juan Iglesias, Hólger Zambrano, Daniel Borja, Carlos Oñate, Francisco Simbaña, Freddy Valencia, Karen Rodríguez De la Vera, Luis Poveda, Fernando Proaño, Lorena Parra & María Chacón

  3. Ministerio del Ambiente del Perú (MINAM), Lima, Perú

    William Llactayo, Tatiana Pequeño Saco, Walter Huamani, German Marchand, Raúl Tinoco & Luis Quispe

  4. Universidad Nacional Federico Villarreal, Lima, Perú

    William Llactayo

  5. Rainforest Alliance (RA), Lima, Perú

    Tatiana Pequeño Saco

  6. Servicio Nacional Forestal y de Fauna Silvestre (SERFOR), Lima, Perú

    Alexs Arana

  7. United Nations Development Programme, New York, NY, USA

    Anne Lucy Stilger Virnig

  8. Programa de las Naciones Unidas del Desarrollo – Perú (PNUD), Lima, Perú

    Patricia Huerta

  9. Independent Contributor, Quito, Ecuador

    Karla Jiménez & Pedro Tipula Tipula

  10. Independent Contributor, Lima, Perú

    Pedro Tipula Tipula

  11. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Bogotá, Colombia

    Susana Rodríguez

  12. GeoIS, Austin, TX, USA

    Rodrigo Sierra

  13. Professor Emeritus of Ecology, Montana State University, Bozeman, MT, USA

    Andrew J. Hansen

Authors

  1. Jose Aragon-Osejo
    View author publications

    Search author on:PubMed Google Scholar

  2. Lenin Beltrán
    View author publications

    Search author on:PubMed Google Scholar

  3. Juan Iglesias
    View author publications

    Search author on:PubMed Google Scholar

  4. Hólger Zambrano
    View author publications

    Search author on:PubMed Google Scholar

  5. Daniel Borja
    View author publications

    Search author on:PubMed Google Scholar

  6. Carlos Oñate
    View author publications

    Search author on:PubMed Google Scholar

  7. Francisco Simbaña
    View author publications

    Search author on:PubMed Google Scholar

  8. Freddy Valencia
    View author publications

    Search author on:PubMed Google Scholar

  9. Karen Rodríguez De la Vera
    View author publications

    Search author on:PubMed Google Scholar

  10. Luis Poveda
    View author publications

    Search author on:PubMed Google Scholar

  11. Fernando Proaño
    View author publications

    Search author on:PubMed Google Scholar

  12. Lorena Parra
    View author publications

    Search author on:PubMed Google Scholar

  13. María Chacón
    View author publications

    Search author on:PubMed Google Scholar

  14. William Llactayo
    View author publications

    Search author on:PubMed Google Scholar

  15. Tatiana Pequeño Saco
    View author publications

    Search author on:PubMed Google Scholar

  16. Walter Huamani
    View author publications

    Search author on:PubMed Google Scholar

  17. German Marchand
    View author publications

    Search author on:PubMed Google Scholar

  18. Raúl Tinoco
    View author publications

    Search author on:PubMed Google Scholar

  19. Luis Quispe
    View author publications

    Search author on:PubMed Google Scholar

  20. Alexs Arana
    View author publications

    Search author on:PubMed Google Scholar

  21. Anne Lucy Stilger Virnig
    View author publications

    Search author on:PubMed Google Scholar

  22. Patricia Huerta
    View author publications

    Search author on:PubMed Google Scholar

  23. Karla Jiménez
    View author publications

    Search author on:PubMed Google Scholar

  24. Pedro Tipula Tipula
    View author publications

    Search author on:PubMed Google Scholar

  25. Susana Rodríguez
    View author publications

    Search author on:PubMed Google Scholar

  26. Rodrigo Sierra
    View author publications

    Search author on:PubMed Google Scholar

  27. Andrew J. Hansen
    View author publications

    Search author on:PubMed Google Scholar

  28. Oscar Venter
    View author publications

    Search author on:PubMed Google Scholar

Contributions

J.A.-O. conducted the analysis and wrote the manuscript. R.S. provided inputs. All co-authors conceived the study and reviewed and edited the manuscript.

Corresponding author

Correspondence to
Jose Aragon-Osejo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Aragon-Osejo, J., Beltrán, L., Iglesias, J. et al. National human footprint maps for Peru and Ecuador.
Sci Data (2025). https://doi.org/10.1038/s41597-025-06301-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41597-025-06301-0

Source: Ecology - nature.com

Range-wide assessment of habitat suitability for jaguars using multiscale species distribution modelling

Respiratory bacteriome and its predicted functional profiles in blue whales (Balaenoptera musculus)

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close