Montrends: A Google Earth engine application for analysing species' habitat suitability over time

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Alírio, J (Author) Other; Garcia, N (Author) Other; Campos, JC (Author) Other; Arenas-Castrod, S (Author) Other; Pôças, I (Author) Other; Duarte, LB (Author) Other; Ana Teodoro (Author) FCUP; Sillero, N (Author) Other

Journal

Title: Ecological Informatics

Vol. 89

ISSN: 1574-9541

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-018-WQ5

DOI: 10.1016/j.ecoinf.2025.103201

Abstract (EN): Human activities are impacting biodiversity worldwide. Biodiversity monitoring is essential to assess and support conservation status and trends. Remote sensing has played a crucial role in supporting biodiversity monitoring, but more intuitive and fast-processing tools are still required to improve biodiversity conservation. Herein, we present a Google Earth Engine (GEE) App called Montreds, which implements a biodiversity monitoring tool to measure trends in species habitat suitability over time by calculating ecological niche models (ENMs) with a time series of satellite products. The application is specific to Montesinho Natural Park/Nogueira Special Conservation Area, a protected area located in northeastern Portugal. The application calculates ENMs over time with MaxEnt for five taxa (vascular plants, amphibians, reptiles, birds, and mammals), using a time series of six Moderate-Resolution Imaging Spectroradiometer (MODIS) products between 2001 and 2023. Habitat suitability trends are estimated using the Mann-Kendall test. The Montrends' main output is a map for each modelled species with positive, negative, or null trends over time. If habitat suitability decreases monotonically over time, the trend is identified as negative. The application allows the users to select the species to be modelled, the temporal period, the number of model replicates, and the proportion of training and test records. The application runs the analyses intuitively in about a minute. Several results are displayed: the mean MaxEnt model over time and the Mann-Kendall trends for the whole study area, the species presences, the pixels with significant trends, and the species' occurrences in significant pixels. The application also provides the main MaxEnt outputs, including Area Under the Curve (AUC) values and variable contributions, plots of the global contributions of predictor variables over time, average trend values, and information on MaxEnt parameters. Decision-makers and conservation planners can use this application as a complementary tool for biodiversity monitoring and conservation.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9