Ecological Niche Models using MaxEnt in Google Earth Engine: Evaluation, guidelines and recommendations

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Campos, JC (Author) Other; Garcia, N (Author) Other; Alirio, J (Author) Other; Arenas-Castro, S (Author) Other; Ana Teodoro (Author) FCUP; sillero, n (Author) FCUP

Journal

Title: Ecological Informatics

Vol. 76

ISSN: 1574-9541

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 15 Citations

Scopus - 18 Citations

Other information

Authenticus ID: P-00Y-HT2

DOI: 10.1016/j.ecoinf.2023.102147

Abstract (EN): Google Earth Engine (GEE) has revolutionized geospatial analyses by fast-processing formerly demanding ana-lyses from multiple research areas. Recently, maximum entropy (MaxEnt), the most commonly used method in ecological niche models (ENMs), was integrated into GEE. This integration can significantly enhance modeling efficiency and encourage multidisciplinary approaches of ENMs, but an evaluation assessment of MaxEnt in GEE is lacking. Herein, we present the first MaxEnt models in GEE, as well as its first statistical and spatial evaluation. We also identify the limitations of the approach, providing guidelines and recommendations for its easier applicability in GEE.We tested MaxEnt in GEE using 11 case studies. For each case, we used species of different taxa (insects, amphibians, reptiles, birds and mammals) distributed across global and regional extents. Each species occupied habitats with distinct environmental characteristics (nine terrestrial and two marine species) and within diver-gent ecoregions across five continents. The models were performed in GEE and Maxent software, and both ap-proaches were contrasted for their model discrimination performance (assessed by eight evaluation metrics) and spatial consistency (correlation analyses and two measures of niche overlap/equivalency).MaxEnt in GEE allows setting several parameters, but important analyses and outputs are unavailable, such as automatic selection of background data, model replicates, and analyses of variable importance (concretely, jackknife analyses and response curves). GEE provided MaxEnt models with high discrimination performance (area under the curve mean between all species models of 0.90) and with spatial equivalency in relation to Maxent software outputs (Hellinger's I mean between all species models >0.90).Our work demonstrates the first application and assessment of MaxEnt in GEE at global and regional scales. We conclude that the GEE modeling method provides ENMs with high performance and reliable spatial pre-dictions, comparable to the widely used Maxent software. We also acknowledge important limitations that should be integrated into GEE in the future, particularly those related to the assessment of variable importance. We expect that our guidelines, recommendations and potential solutions to surpass the identified limitations could help researchers easily apply MaxEnt in GEE across different research fields.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14