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Climate REconstruction SofTware (CREST) [download]

The CREST method [1] is related to a Bayesian approach that combines presence-only occurrence data and modern climatologies to estimate the conditional response of a given taxon to a variable of interest. Taking the form of probability density functions (pdfs), these links are fitted in one or two steps based on the nature of the proxy being studied. In simple cases, where fossils can be identified at species level (e.g. foraminifers, plant macrofossils), the pdfs are defined by unimodal and parametric functions (e.g. normal or log-normal distributions depending of the nature of the studied variable, see [1] for a more detailed discussion).


Modern distributions of the four species of Tribulus found in southern Africa, and the associated normally distributed pdfs fitted for each species.

The parameters (e.g. a mean and a standard deviation in the case of a normal or log-normal distribution) describing these distributions are estimated from the ensemble of climate values corresponding to the presence records, each being weighted as an inverse function of its abundance in the study area. This correction is needed to remove the influence of the heterogeneously distributed modern climate space and ensure that the optimum exhibited by the pdf truly reflects the climatic preference of the species, rather than the modern abundance of a given climate value [2-3]


Combination of the four species pdfs (colours corresponding to the maps above) into the Mean Annual Temperature pdf of the pollen taxon Tribulus in southern Africa.

    When the fossils cannot be identified at the species level (e.g. pollen grains identified at the genus or family level), two steps are necessary to create the taxon-climate probabilistic link. First, following the aforementioned process, a parametric pdf is created for each of the species producing the same type of pollen grains, and then, these pdfs are grouped together to create a higher order pdf representing the pollen type, with each species being weighted as a function of the extent of its distribution. One assumption of the model is that in the absence of independent evidence, species with larger distributions are considered more likely to have produced the pollen grain observed. No additional assumptions are made concerning the shapes of these pollen pdfs, allowing them to be multimodal if different species/groupings exhibit different climate requirements. It is worth noting that because CREST uses parametric functions to define the species pdfs, the process can be used with incomplete distribution data. Recent results suggest that robust pdfs can still be obtained from truncated geographical distributions, provided that the full range of the climatic tolerance of the species is well covered in the climate space [4-7].

    Finally, to estimate past climate parameters, the pdfs for each fossil taxon identified in a sample are multiplied together, each with a weight that is derived from the observed percentages. Since relative abundances, different production rates and

taphonomy are important factors influencing the fossil assemblage observed, direct percentages cannot be used directly, and they need to be transformed to minimize the effect of all these factors. In CREST, it is done independently for each taxon by normalizing the raw percentages by the average of the percentages observed in a sequence (zeros excluded). A value higher (lower) than one suggests that the climate at the time of deposition was more (less) favourable (i.e. closer to the taxon’s climate optimum) than the average climate in which the taxon has been observed during the studied period. The multiplication of pdfs results in a posterior distribution of probabilities along the climate gradient, from which climate estimates and uncertainties can be derived. More details about the method can be obtained from the original publication [1].

    The probabilistic nature of CREST also provides the unique opportunity to obtain reconstructions in the form of posterior distributions of probabilities that describe the likelihood of all the climate values along a studied climate gradient, and not just a single ‘best estimate’ associated with a standard error.


Multiplication of N different pollen pdfs (in colours). The resulting posterior distribution is in white.

To remain informed of any update concerning the CREST methodology and software, I invite you to join the mailing list.


The CREST sofware is a point-and-click graphical user interface designed to automatically apply the method described above. It is fully compatible with the GBIF for CREST database, (available here). Theoretically, it should also work with any MySQL, PostGreSQL, Microsoft Access and sqlite3 databases, provided that the data can be extracted in a way that CREST can read (look at the manual for more deals).

The software is still  under development and feedbacks (positive and negative!) are welcome.

If you are using this software, please cite these two papers:

  • Chevalier, M., Cheddadi, R., Chase, B.M., 2014. CREST (Climate REconstruction SofTware): a probability density function (PDF)-based quantitative climate reconstruction method. Clim. Past 10, 2081–2098. doi:10.5194/cp-10-2081-2014

  • Chevalier, M., 2019. Enabling possibilities to quantify past climate from fossil assemblages at a global scale. Glob. Planet. Change 175, 27–35. DOI: 10.1016/j.gloplacha.2019.01.016

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Download CREST

The CREST Software is obsolete and will not be updated anymore. I am currently transferring the reconstruction framework into an R package that can be access from my github page. 

Download CREST v4.1.0

[1] Chevalier, M., Cheddadi, R., Chase, B.M., 2014. CREST (Climate REconstruction SofTware): a probability density function (PDF)-based quantitative climate reconstruction method. Clim. Past 10, 2081–2098. doi:10.5194/cp-10-2081-2014

[2] Kühl, N., Gebhardt, C., Litt, T., Hense, A., 2002. Probability Density Functions as Botanical-Climatological Transfer Functions for Climate Reconstruction. Quat. Res. 58, 381–392. doi:10.1006/qres.2002.2380

[3] Bray, P.J., Blockley, S.P.E., Coope, G.R., Dadswell, L.F., Elias, S.A., Lowe, J.J., Pollard, A.M., 2006. Refining mutual climatic range (MCR) quantitative estimates of palaeotemperature using ubiquity analysis. Quat. Sci. Rev. 25, 1865–1876. doi:10.1016/j.quascirev.2006.01.023

[4] Chevalier, M., Chase, B.M., 2015. Southeast African records reveal a coherent shift from high- to low-latitude forcing mechanisms along the east African margin across last glacial–interglacial transition. Quat. Sci. Rev. 125, 117–130. doi:10.1016/j.quascirev.2015.07.009

[5] Chevalier, M., Chase, B.M., 2016. Determining the drivers of long-term aridity variability: a southern African case study. J. Quat. Sci. 31, 143–151. doi:10.1002/jqs.2850

[6] Lim, S., Chase, B.M., Chevalier, M., Reimer, P.J., 2016. 50,000 years of climate in the Namib Desert, Pella, South Africa. Palaeogeogr. Palaeoclimatol. Palaeoecol. 451, 197–209. doi:10.1016/j.palaeo.2016.03.001

[7] Cordova, C.E., Scott, L., Chase, B.M., Chevalier, M., 2017. Late Pleistocene-Holocene vegetation and climate change in the Middle Kalahari, Lake Ngami, Botswana. Quat. Sci. Rev. 171, 199–215. doi:10.1016/j.quascirev.2017.06.036

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