Spatial weed distribution models under climate change: a short review
Climate change is a concern worldwide that could trigger many changes with severe consequences. Since human demography is steadily increasing, agriculture has to be constantly investigated to aim at improving its efficiency. Weeds play a key role in this task, especially in the recent past and at present, when new introductions have been favoured by a rise in tourism and international trade. To obtain knowledge relating weeds and their behaviour to climate change, species distribution models (SDMs) have also increased recently. In this work, we have reviewed some articles published since 2017 on modelled weeds, aiming to give a response to, among other things, the species most studied, the scale and location of the studies, the algorithms used and validation parameters, global change scenarios, types of variables, and the sources from which the data were collected. Fifty-nine articles were selected to be reviewed, with maximum entropy (MaxEnt) and area under the curve (AUC) being the most popular software and validation processes. Environmental and topographic variables were considered above pedological and anthropogenic ones. Europe was the continent and China, the USA, and India the countries most studied. In this review, it was found that the number of published articles between developed and developing countries is unbalanced and comes out in favour of the former. The current knowledge on this topic can be considered to be good not enough, especially in developing countries with high population densities. The more knowledge we can obtain, the better our understanding is of how to deal with this issue, which is a worldwide preoccupation.
| Main Authors: | , |
|---|---|
| Format: | artículo de revisión biblioteca |
| Language: | English |
| Published: |
PeerJ
2023-04-10
|
| Subjects: | Weeds, Ageratina adenophora, Ambrosia artemisiifolia, Invasive species, Mikania micrantha, Parthenium hysterophorus, Species Distribution Models (SDMs), |
| Online Access: | http://hdl.handle.net/10261/349817 https://api.elsevier.com/content/abstract/scopus_id/85156142117 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|