Microclimf: fast modelling of microclimate across real landscapes in R

2025 · doi:10.32942/x2bd17
preprint OA: closed CC-BY-4.0
🔓 Open OA copy Full text JSON View at publisher
⚙ AI-generated deep summary by claude@2026-07, 2026-07-03 · read from full text ⓘ

The paper introduces microclimf, a mechanistic, computationally efficient microclimate modelling framework with an R interface and C++ backend, aimed at generating gridded estimates of temperature, humidity, wind speed, and radiation fluxes within and below vegetation canopies. Using readily available spatial inputs, the model combines a simplified Lagrangian canopy component and an optional snow model, and is designed for large-area processing at user-defined spatial and temporal resolutions. Validation across boreal and tropical forest environments showed strong agreement with in-situ temperature measurements (RMSE 0.69–2.9 °C), with the key stated limitation being that evaluation reported is primarily based on temperature rather than the full set of output variables. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

1. Many ecological studies require climate data, but readily available datasets are poor surrogates for the conditions that organisms experience in nature. Understanding the climatic conditions experienced by organisms requires modelling microclimate rather than relying on coarse, station-based climate data. 2. I present microclimf, a mechanistic microclimate model designed for computationally efficient, gridded estimation of microclimate, within, and below vegetation canopies. The model is written in C++ with an R front end and requires only readily available spatial datasets as inputs. It incorporates a simplified Lagrangian canopy model, an optional snow model, and routines for efficient large-area processing at user-defined spatial and temporal resolutions. Outputs include temperature, humidity, wind speed and radiation fluxes. 3. Validation across diverse environments—including boreal and tropical forests—showed strong agreement with in-situ temperature measurements (RMSE 0.69–2.9 °C), demonstrating the model’s utility for ecological applications requiring fine-scale climatic data. 4. The package addresses the need for improved estimation of regional and landscape--scale predictions of the conditions experienced by organisms, thereby facilitating more robust understanding and prediction of species responses to climatic changes.
Full text 2,166 characters · extracted from oa-doi-fallback · 2 sections · click to expand

Abstract

1. Many ecological studies require climate data, but readily available datasets are poor surrogates for the conditions that organisms experience in nature. Understanding the climatic conditions experienced by organisms requires modelling microclimate rather than relying on coarse, station-based climate data. 2. I present microclimf, a mechanistic microclimate model designed for computationally efficient, gridded estimation of microclimate, within, and below vegetation canopies. The model is written in C++ with an R front end and requires only readily available spatial datasets as inputs. It incorporates a simplified Lagrangian canopy model, an optional snow model, and routines for efficient large-area processing at user-defined spatial and temporal resolutions. Outputs include temperature, humidity, wind speed and radiation fluxes. 3. Validation across diverse environments—including boreal and tropical forests—showed strong agreement with in-situ temperature measurements (RMSE 0.69–2.9 °C), demonstrating the model’s utility for ecological applications requiring fine-scale climatic data. 4. The package addresses the need for improved estimation of regional and landscape--scale predictions of the conditions experienced by organisms, thereby facilitating more robust understanding and prediction of species responses to climatic changes. DOI https://doi.org/10.32942/X2BD17 Subjects Biochemistry, Biophysics, and Structural Biology, Climate, Ecology and Evolutionary Biology

Keywords

biodiversity, biophysical ecology, climate change, modelling, species distribution model Dates Published: 2025-05-08 20:26 Last Updated: 2025-05-08 20:26 License CC BY Attribution 4.0 International Additional Metadata Conflict of interest statement: The author declares no conflicts of interest Data and Code Availability Statement: Logger data used to test the model and data used to generate Fig. 2 are available from Zenodo (https://doi.org/10.5281/zenodo.15364781 & 10.5281/zenodo.8338611). Other datasets used are included with ‘microclimf’ R package available from https://github.com/ilyamaclean. Language: English

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-05-22T02:00:06.705733+00:00
License: CC-BY-4.0