Hi, I'm Miguel Aristizabal

The páramo's shift over the centuries

Animated reconstruction of Andean páramo climatic suitability over the last 22,000 years. Colors show MaxEnt-predicted suitability (0-1) from a CHELSA bioclim ^[1,2] model trained on random páramo points, projected through CHELSA-TraCE21k ^[3,4] paleoclimate and draped over exaggerated Andean topography.

This animation shows how climatic suitability for the páramo biome may have shifted since 22 ka BP (22,000 years before present) to today. For each time slice (~100-year steps), a MaxEnt (maxnet) SDM trained on random points within the páramo domain is projected onto CHELSA-TraCE21k ^[3,4] paleoclimate bioclim variables. Suitability (0–1) is mapped as a color overlay and draped over a 3D digital elevation model with vertical exaggeration to emphasize mountain structure and changing high-elevation climate space. The 500 training points were sampled from the intersection of the páramo extent in the Olson et al. (2001) ^[5] terrestrial ecoregions and the Peyre et al. (2021) ^[6] páramo land-cover classification.

More detail & caveats

The goal of this visualization is to show climate-driven changes in the potential distribution of páramo-like conditions, not any single species. I fit a MaxEnt (maxnet) model using CHELSA bioclim (bio01-bio19) ^[1,2] predictors and a training set of random points constrained to the páramo region (something of a biome-level "páramo climate envelope" approach). That model was then projected across the CHELSA-TraCE21k ^[3,4] hindcast (22 ka BP to present) to produce a time series of continuous suitability values (0–1). The resulting rasters are rendered as a colormap and draped onto a vertically-exaggerated DEM, allowing us to see how high-Andean climatic suitability expands, contracts, and shifts through late-glacial and Holocene climate change.

As with any hindcast SDM, interpretation should be cautious: the output reflects modeled climatic similarity to the training domain and carries inherent uncertainty.

References

1. Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the earth's land surface areas. Scientific Data, 4(1), 170122. doi:10.1038/sdata.2017.122
2. Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2021). Climatologies at high resolution for the earth's land surface areas [Dataset]. EnviDat. doi:10.16904/envidat.228
3. Karger, D. N., Nobis, M. P., Normand, S., Graham, C. H., & Zimmermann, N. E. (2020). CHELSA-TraCE21k: Downscaled transient temperature and precipitation data since the last glacial maximum. EnviDat. doi:10.16904/envidat.211
4. Karger, D. N., Nobis, M. P., Normand, S., Graham, C. H., & Zimmermann, N. E. (2023). CHELSA-TraCE21k – high-resolution (1 km) downscaled transient temperature and precipitation data since the Last Glacial Maximum. Climate of the Past, 19(2), 439–456. doi:10.5194/cp-19-439-2023
5. Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V., Underwood, E. C., … & Kassem, K. R. (2001). Terrestrial Ecoregions of the World: A New Map of Life on Earth. BioScience, 51(11), 933–938. doi:10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2
6. Peyre, G., Osorio, D., François, R., & Anthelme, F. (2021). Mapping the páramo land-cover in the Northern Andes. International Journal of Remote Sensing, 42(20), 7777–7797. doi:10.1080/01431161.2021.1964709