Frescalo

Romain Goury | July 29, 2025

Table of Contents

1. Description & principle
2. Reference articles
3. Packages
4. Assessment table

Description & principle

Frescalo is a method developed by Hill (2012), that aims to consider and correct for spatio-temporal biases from unstructured (i.e., opportunistic) data. The method provides estimated of temporal trends when there has been enough sampling to at least estimate species’ local relative frequencies accurately. The Frescalo algorithm is divided in two main steps such as a spatial and temporal correction (see Fig. 1). The first step aims to correct for variation in sampling effort across neighbourhoods for the overall time period being considered, while the second step relies in correcting for time-period specific variations in recording effort within and across sites.

Figure 1 An overview of the Frescalo method (Goury et al., 2025)

Further online resources

• sparta R vignette on Frescalo

Reference articles

Method

• Local frequency as a key to interpreting species occurrence data when recording effort is not known (Hill, 2012)
• A practical guide to species trend detection with unstructured data using local frequency scaling (Frescalo) (Goury et al., 2025)

Research applications

With RS data in Ecology / Biodiversity

• Montràs-Janer et al. (2024)

Without RS data (Ecology domain)

• Auffret & Svenning (2022)
• Eichenberg et al. (2021)
• Fox et al. (2014)
• Society (2014)
• Suggitt et al. (2023)
• White et al. (2019)
• Pescott et al. (2019)
• Eichenberg et al. (2021)
• Redhead et al. (2018)
• Dyer et al. (2017)
• Goury et al. (2025)

Packages

Python

No packages in python are existing

R

• https://www.brc.ac.uk/biblio/frescalo-computer-program-analyse-your-biological-records (old package version)

• https://github.com/colinharrower/frescalo (package published in Goury et al., 2025)

Code Cells

• https://github.com/colinharrower/frescalo
• https://agauffret.github.io/FrescaloFun/FrescaloFun_tutorial250319.html (from the old package)
• https://rpubs.com/sacrevert/simpleFresDemo (old package)
• https://biologicalrecordscentre.github.io/sparta/articles/vignette.html (some example for the old version)

Assessment table

Category	Criteria	Assessment
Outcome	Objective	Effect estimation, Detection
	Estimand	Inapplicable
	Validity	Inapplicable
Data compatibility	Type	Spatial only (cross-sectional), Panel data (many samples)
	Required TS length	Handles ≤ 10, ≥ 10, ≥ 100
	Handles few samples	No
	Handles huge datasets (n)	Yes
	Handles missing data	Yes
	RS-data proven	Few applications
Assumptions	Functional form	Inapplicable
	No unobserved confounders	Inapplicable
	No interference	Inapplicable
	Well-defined treatments	Inapplicable
	Common support (positivity)	Inapplicable
	Causal Markov Condition	Inapplicable
	Faithfulness	Inapplicable
	IDD	Inapplicable
	Model specific assumption	Unevaluated
Model properties	Requires explicit processes	Yes
	Exposure type	Continuous / Time-varying
	Number of variables	High-dimensional (p≫n)
	Handles lag effects	Inapplicable
	Propagates uncertainty	Inherent capacity
	Parametric nature	Inapplicable
Packages	Language	R
	Usage	Sparse documentation

References

1. Hill, M. O. (2012). Local Frequency as a Key to Interpreting Species Occurrence Data When Recording Effort Is Not Known. Methods in Ecology and Evolution, 3, 195–205. https://doi.org/10.1111/j.2041-210X.2011.00146.x
2. Goury, R., Bowler, D. E., Harrower, C., Münkemüller, T., Vallet, J., Yearsley, J., Thuiller, W., & Pescott, O. L. (2025). A Practical Guide to Species Trend Detection with Unstructured Data Using Local Frequency Scaling (Frescalo). Submitted to Ecography. https://ecoevorxiv.org/repository/view/9467/
3. Montràs-Janer, T., Suggitt, A. J., Fox, R., Jönsson, M., Martay, B., Roy, D. B., Walker, K. J., & Auffret, A. G. (2024). Anthropogenic Climate and Land-Use Change Drive Short- and Long-Term Biodiversity Shifts across Taxa. Nature Ecology & Evolution, 8, 739–751. https://doi.org/10.1038/s41559-024-02326-7
4. Auffret, A. G., & Svenning, J.-C. (2022). Climate Warming Has Compounded Plant Responses to Habitat Conversion in Northern Europe. Nature Communications, 13, 7818. https://doi.org/10.1038/s41467-022-35516-7
5. Eichenberg, D., Bowler, D. E., Bonn, A., Bruelheide, H., Grescho, V., Harter, D., Jandt, U., May, R., Winter, M., & Jansen, F. (2021). Widespread Decline in Central European Plant Diversity across Six Decades. Global Change Biology, 27, 1097–1110. https://doi.org/10.1111/gcb.15447
6. Fox, R., Oliver, T. H., Harrower, C., Parsons, M. S., Thomas, C. D., & Roy, D. B. (2014). Long-Term Changes to the Frequency of Occurrence of British Moths Are Consistent with Opposing and Synergistic Effects of Climate and Land-Use Changes. Journal of Applied Ecology, 51, 949–957. https://doi.org/10.1111/1365-2664.12256
7. Society, B. B. (2014). Atlas of British and Irish Bryophytes. In British Bryological Society. https://www.britishbryologicalsociety.org.uk/publications/atlas-of-british-and-irish-bryophytes/
8. Suggitt, A. J., Wheatley, C. J., Aucott, P., Beale, C. M., Fox, R., Hill, J. K., Isaac, N. J. B., Martay, B., Southall, H., Thomas, C. D., Walker, K. J., & Auffret, A. G. (2023). Linking Climate Warming and Land Conversion to Species’ Range Changes across Great Britain. Nature Communications, 14, 6759. https://doi.org/10.1038/s41467-023-42475-0
9. White, H. J., Gaul, W., Sadykova, D., León-Sánchez, L., Caplat, P., Emmerson, M. C., & Yearsley, J. M. (2019). Land Cover Drives Large Scale Productivity-Diversity Relationships in Irish Vascular Plants. PeerJ, 7, e7035. https://doi.org/10.7717/peerj.7035
10. Pescott, O. L., Humphrey, T. A., Stroh, P. A., & Walker, K. J. (2019). Temporal Changes in Distributions and the Species Atlas: How Can British and Irish Plant Data Shoulder the Inferential Burden? British & Irish Botany, 1, 250–282. https://doi.org/10.33928/bib.2019.01.250
11. Redhead, J. W., Woodcock, B. A., Pocock, M. J. O., Pywell, R. F., Vanbergen, A. J., & Oliver, T. H. (2018). Potential Landscape-Scale Pollinator Networks across Great Britain: Structure, Stability and Influence of Agricultural Land Cover. Ecology Letters, 21, 1821–1832. https://doi.org/10.1111/ele.13157
12. Dyer, R. J., Gillings, S., Pywell, R. F., Fox, R., Roy, D. B., & Oliver, T. H. (2017). Developing a Biodiversity-Based Indicator for Large-Scale Environmental Assessment: A Case Study of Proposed Shale Gas Extraction Sites in Britain. Journal of Applied Ecology, 54, 872–882. https://doi.org/10.1111/1365-2664.12784
13. Goury, R., Thuiller, W., Abdulhak, S., Pache, G., Van Es, J., Bowler, D. E., Renaud, J., Violle, C., & Münkemüller, T. (2025). Recent vegetation shifts in the French Alps with winners outnumbering losers. In Submitted to Journal of Ecology.