Abstract
The application of machine learning (ML) for predicting species habitat suitability has become increasingly popular. However, using a single ML algorithm may not provide optimal predictions for a given dataset, making it challenging to achieve high accuracy. Therefore, this study proposes a novel approach to assess habitat suitability of three redstarts species (P. Moussieri, P. Ochruros, and P. Phoenicurus) based on ensemble learning techniques. Initially, eight ML models namely MLP, SVM, KNN, Decision Trees (DT), Gradient Boosting Classifier (GB), Random Forest (RF), AdaBoost (AB), and Quadratic Discriminant Analysis (QDA) were trained individually. Then, based on the diversity of these base-learners, seven heterogeneous ensembles of two up to eight models were constructed for each species dataset. This study presents a thorough modeling framework for implementing heterogeneous ensembles and enhancing the overall comprehension of their performance in comparison to single models. The performance of this experiment was evaluated using: (1) six performance measures (AUC, sensitivity, specificity, accuracy, Kappa, and TSS), (2) Borda Count ranking method, and (3) Scott Knott statistical test. Results showed the potential of heterogeneous ensembles for predicting habitat suitability of the three redstarts birds. The proposed ensembles consistently outperformed single models across all datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
IUCN/SSC, Strategic Planning for Species Conservation: A Handbook. Version 1.0 (2008)
Koh, L.P., Dunn, R.R., Sodhi, N.S., Colwell, R.K., Proctor, H.C., Smith, V.S.: Species coextinctions and the biodiversity crisis. Science 305(5690), 1632–1634 (2004). https://doi.org/10.1126/science.1101101
Lawler, J.J., Wiersma, Y.F., Huettmann, F.: Using species distribution models for conservation planning and ecological forecasting. In: Predictive Species and Habitat Modeling in Landscape Ecology: Concepts and Applications (2011). https://doi.org/10.1007/978-1-4419-7390-0_14
Padonou, E.A., Teka, O., Bachmann, Y., Schmidt, M., Lykke, A.M., Sinsin, B.: Using species distribution models to select species resistant to climate change for ecological restoration of bowé in West Africa. Afr. J. Ecol. 53(1), 83–92 (2015). https://doi.org/10.1111/aje.12205
Guisan, A., Thuiller, W.: Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8(9), 993–1009 (2005). https://doi.org/10.1111/j.1461-0248.2005.00792.x
El Alaoui, O., Idri, A.: Predicting the potential distribution of wheatear birds using stacked generalization-based ensembles. Ecol. Inform. 75, 102084 (2023). https://doi.org/10.1016/j.ecoinf.2023.102084
El Alaoui, O., Idri, A.: Heterogeneous ensemble learning for modelling species distribution: a case study of redstarts habitat suitability, pp. 105–114, July 2023. https://doi.org/10.5220/0012118100003541
Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms (2012). https://doi.org/10.1201/b12207
Kaky, E., Nolan, V., Alatawi, A., Gilbert, F.: A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: a case study with Egyptian medicinal plants. Ecol. Inform. 60, 101150 (2020). https://doi.org/10.1016/j.ecoinf.2020.101150
Früh, L., Kampen, H., Kerkow, A., Schaub, G.A., Walther, D., Wieland, R.: Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations. Ecol. Model. 388, 136–144 (2018). https://doi.org/10.1016/j.ecolmodel.2018.08.011
Dong, J.Y., Hu, C., Zhang, X., Sun, X., Zhang, P., Li, W.T.: Selection of aquaculture sites by using an ensemble model method: a case study of Ruditapes philippinarums in Moon Lake. Aquaculture 519, 734897 (2020). https://doi.org/10.1016/j.aquaculture.2019.734897
GBIF.Org User, “Occurrence Download.” The Global Biodiversity Information Facility (2023). https://doi.org/10.15468/DL.M8EFVG
Fick, S.E., Hijmans, R.J.: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017). https://doi.org/10.1002/joc.5086
CGIAR-CSI, SRTM 90 m Digital Elevation Database v4.1, Consortium for Spatial Information
Guélat, J., Kéry, M.: Effects of spatial autocorrelation and imperfect detection on species distribution models. Methods Ecol. Evol. 9(6), 1614–1625 (2018). https://doi.org/10.1111/2041-210X.12983
Kraskov, A., Stögbauer, H., Grassberger, P.: Estimating mutual information. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 69(6) (2004). https://doi.org/10.1103/PhysRevE.69.066138
Kuncheva, L.I., Whitaker, C.J.: Ten measures of diversity in classifier ensembles: limits for two classifiers. IEE Colloquium (Digest) 50, 73–82 (2001). https://doi.org/10.1049/ic:20010105
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
El Alaoui, O., Idri, A. (2024). Habitat Suitability Assessment of Three Passerine Birds Using Ensemble Learning with Diverse Models. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Marańda, A. (eds) Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024. Lecture Notes in Networks and Systems, vol 987. Springer, Cham. https://doi.org/10.1007/978-3-031-60221-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-60221-4_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-60220-7
Online ISBN: 978-3-031-60221-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)