PreprintReviewVersion 1Preserved in Portico This version is not peer-reviewed
From Bioclimatic Envelopes to Machine Learning: A Journey Through the History, Present, and Future of Species Distribution Modeling With Practical Tips for Use and Notes to Bryophytes
Version 1
: Received: 14 April 2023 / Approved: 14 April 2023 / Online: 14 April 2023 (10:35:42 CEST)
How to cite:
Číhal, L. From Bioclimatic Envelopes to Machine Learning: A Journey Through the History, Present, and Future of Species Distribution Modeling With Practical Tips for Use and Notes to Bryophytes. Preprints2023, 2023040367. https://doi.org/10.20944/preprints202304.0367.v1
Číhal, L. From Bioclimatic Envelopes to Machine Learning: A Journey Through the History, Present, and Future of Species Distribution Modeling With Practical Tips for Use and Notes to Bryophytes. Preprints 2023, 2023040367. https://doi.org/10.20944/preprints202304.0367.v1
Číhal, L. From Bioclimatic Envelopes to Machine Learning: A Journey Through the History, Present, and Future of Species Distribution Modeling With Practical Tips for Use and Notes to Bryophytes. Preprints2023, 2023040367. https://doi.org/10.20944/preprints202304.0367.v1
APA Style
Číhal, L. (2023). From Bioclimatic Envelopes to Machine Learning: A Journey Through the History, Present, and Future of Species Distribution Modeling With Practical Tips for Use and Notes to Bryophytes. Preprints. https://doi.org/10.20944/preprints202304.0367.v1
Chicago/Turabian Style
Číhal, L. 2023 "From Bioclimatic Envelopes to Machine Learning: A Journey Through the History, Present, and Future of Species Distribution Modeling With Practical Tips for Use and Notes to Bryophytes" Preprints. https://doi.org/10.20944/preprints202304.0367.v1
Abstract
Species distribution modeling (SDM) has come a long way since its inception. Starting as simple bioclimatic envelope models based on expert knowledge, species distribution models (SDMs) have evolved into complex and sophisticated models that incorporate multiple sources of data and machine learning algorithms. Today, SDMs play a crucial role in addressing pressing conservation and management issues, including the impacts of climate change on species ranges and the as-sessment of species vulnerability to extinction. In this article, we will embark on a journey through the history, present, and future of SDM, exploring its evolution from bioclimatic envelopes to machine learning. We will also provide practical tips on how to use SDMs effectively and discuss the exciting future developments in this field. Whether you are a seasoned SDM expert or new to this field, this article will provide valuable insights into the exciting world of SDM. By exploring the rich history and current state of the field, we hope to shed light on the tremendous potential of SDM for improving our understanding of the distribution of species in a changing world.
Keywords
species distribution modeling; machine learning; MaxEnt; bryophytes; climatic change
Subject
Biology and Life Sciences, Plant Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
