<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Modeling potential habitat for plant species is an appropriate approach to maintain biodiversity, developing proper reforestation campaigns, and rehabilitating ecosystems. In this study, we investigated the potential distributions of four forest species, namely, <italic>Quercus faginea</italic> Lam.; <italic>Q. ilex</italic> L.; <italic>Tetraclinis articulata</italic> (Vahl) Mast.; and <italic>Pistacia atlantica</italic> Desf. In the north-western Algeria at Saïda region. The MAXENT method was used to model the habitats of these species using topographic data as predictive variables at a resolution of 100 m. Moreover, the model evaluation process was achieved using the area under the operating characteristic curve of the receiver (AUC) and Jackknife test.</p>
<p>The generated models were found to be accurate. AUC results are ranging between 0.98 and 0.91 for the training set and 0.87 and 0.97 for the testing set. The results of the distribution probability of this study provide a useful tool for the local decision-makers of reforestation campaigns.</p>
</abstract>

Modeling potential habitat for plant species is an appropriate approach to maintain biodiversity, developing proper reforestation campaigns, and rehabilitating ecosystems. In this study, we investigated the potential distributions of four forest species, namely, Quercus faginea Lam.; Q. ilex L.; Tetraclinis articulata (Vahl) Mast.; and Pistacia atlantica Desf. In the north-western Algeria at Saïda region. The MAXENT method was used to model the habitats of these species using topographic data as predictive variables at a resolution of 100 m. Moreover, the model evaluation process was achieved using the area under the operating characteristic curve of the receiver (AUC) and Jackknife test.
The generated models were found to be accurate. AUC results are ranging between 0.98 and 0.91 for the training set and 0.87 and 0.97 for the testing set. The results of the distribution probability of this study provide a useful tool for the local decision-makers of reforestation campaigns.

Predicting suitable habitats of the major forest trees in the Saïda region (Algeria): A reliable reforestation tool

Laboratory of Water Resources and Environment

Department of Biology, Faculty of Science

Ekológia (Bratislava)

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Modeling potential habitat for plant species is an appropriate approach to maintain biodiversity, developing proper reforestation campaigns, and rehabilitating...

Predicting suitable habitats of the major forest trees in the Saïda region (Algeria): A reliable reforestation tool

Online veröffentlicht: 17. Okt. 2022

Seitenbereich: 236 - 246

Eingereicht: 03. Okt. 2021

Akzeptiert: 25. Jan. 2022

DOI: https://doi.org/10.2478/eko-2022-0024

Schlüsselwörter
Spatial Distribution Model, MAXENT, reforestation, Saïda, topographic variables

© 2022 Mohammed Djebbouri et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Predicting suitable habitats of the major forest trees in the Saïda region (Algeria): A reliable reforestation tool

Online veröffentlicht: 17. Okt. 2022

Seitenbereich: 236 - 246

Eingereicht: 03. Okt. 2021

Akzeptiert: 25. Jan. 2022

DOI: https://doi.org/10.2478/eko-2022-0024

SchlüsselwörterSpatial Distribution Model, MAXENT, reforestation, Saïda, topographic variables

© 2022 Mohammed Djebbouri et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Spatial Distribution Model, MAXENT, reforestation, Saïda, topographic variables