Ecological niche modelling using MaxEnt for riparian species in a Mediterranean context
Despite their key environmental role, riparian tree and shrub species gained little attention in ecological niche modeling (ENM), especially in semi-arid environments.This study examines the performance of selected climatic, topographic, and geographic predictors in ENM of obligate and non-obligate...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25000962 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Despite their key environmental role, riparian tree and shrub species gained little attention in ecological niche modeling (ENM), especially in semi-arid environments.This study examines the performance of selected climatic, topographic, and geographic predictors in ENM of obligate and non-obligate riparian tree and shrub species in perennial and intermittent streams in the Mediterranean biome.MaxEnt algorithm was used for ENM. Three models were designed with different sets of predictors with cropped and non-cropped backgrounds around the riparian zone.The models generated different predicted distribution maps by species and compared them with the presence points of the studied species. All models showed satisfactory results, with the third model with a non-cropped background and an exhaustive list of predictors showing the highest performance and providing accurate maps, especially when compared to the first run with a cropped background around the riparian zone and the omission of distance from the riverbank and the sea from the predictors used. Predictors such as the river flow regime, the distance from the riverbank, the Emberger Quotient, and the mean of the minimal temperature of the coldest month were essential for the predicted distribution of the selected species.The order of contribution of each predictor in the model enabled us to validate the grouping of species into obligate and non-obligate riparian and conclude which predictors to select for ENM based on the species’ nature. The results could be suggested for red listing assessment of riparian tree species and appropriate species selection for ecosystem restoration. |
|---|---|
| ISSN: | 1470-160X |