Remotely sensed data contribution in predicting the distribution of native Mediterranean species

Abstract

The global change threats significantly alters the ecological distribution of species across different ecosystems. Species distribution models (SDMs) are considered a widely used tool for assessing the global impact on biodiversity. Recently, remote sensing data have been used in a growing number of studies to predict species distribution and improve SDMs performance. This study evaluates the contribution of spectral indices in species distribution modeling using MaxEnt. We compared models based on spectral indices data (RS-only), environmental variables (EN-only), and their combination (CM) to predict the distribution of three key Mediterranean native species: Thymelaea hirsuta, Ononis vaginalis, and Limoniastrum monopetalum. The combined models (CM) demonstrated superior performance with excellent accuracy measures values compared to other models. Jackknife tests revealed both environmental factors (e.g., distance to coastline, mean temperature of wettest and driest quarters) and spectral indices (e.g., NDWI, LST) contributed substantially to predicting the studied species. The findings emphasize the importance of integrating diverse data sources to improve the accuracy of SDMs, particularly in heterogeneous landscapes like the Mediterranean region. This integrated approach provides a more comprehensive understanding of species spreading patterns and is critical for effective management and conservation strategies.

Keywords: Climate change; Conservation planning; Habitat characterization; MODIS data; Maxent; Species distribution modeling (SDM).

Fig. 1

Study area surveyed for the occurrence of the studied species indicating locations of the collected occurrence records and pictures of the studied species (a) Thymelaea hirsuta (green circle), (b) Ononis vaginalis (yellow circle) and (c) Limoniastrum monopetalum (blue circle) collected through field surveys. The maps in figure were produced by the authors within the framework of the GIS software package ArcMap 10.2 (ESRI, 2013). The photographs were taken by the authors.

Fig. 2

Flowchart depicting the spatial analysis and statistical processing steps used in the modeling workflow.

Fig. 3

Response curves of important predictors for Thymelaea hirsuta species distribution model by Maxent using CM, EN-only and RS-only models.

Fig. 4

Response curves of important predictors for Ononis vaginalis species distribution model by Maxent using CM, EN-only and RS-only models.

Fig. 5

Response curves of important predictors for Limoniastrum monopetalum species distribution model by Maxent using CM, EN-only and RS-only model.

Fig. 6

The predicted potential distribution of the studied species by Maxent model under current climate conditions, (a) Thymelaea hirsuta (RS-only), (b) Thymelaea hirsuta (EN-only), (c) Thymelaea hirsuta (CM), (d) Ononis vaginalis (RS-only), (e) Ononis vaginalis (EN-only), (f) Ononis vaginalis (CM), (g) Limoniastrum monopetalum (RS-only), (h) Limoniastrum monopetalum (EN-only) and (i) Limoniastrum monopetalum (CM). The map in figure was produced by the authors through processing of both remote sensing data and environmental data. The remote sensing MODIS satellite data were downloaded from https://earthexplorer.usgs.gov. The mean of the monthly values for each spectral indices for the period from 1/1/2021 to 1/1/2022 was calculated and used in modelling the distribution of the studied species. The environmental variables representing the current climatic conditions used for the construction of the species distribution model were acquired from the World Climate Database (Fick and Hijmans 2017; http://worldclim.org/version2). The bioclimatic data were downloaded from the WorldClim dataset version 2.1 at 30 arc-seconds (1 km) spatial resolution. More details on the data used in the analysis and production of the figures are included in the “Methods” sections.