Mainstreaming remotely sensed ecosystem functioning in ecological niche models

Abstract

Biodiversity is declining globally at unprecedented rates. Ecological niche models (ENMs) are one of the most widely used toolsets to appraise global change impacts on biodiversity. Here, we identify a variety of advantages of incorporating remotely sensed ecosystem functioning attributes (EFAs) into ENMs. The development of ENMs that explicitly incorporate ecosystem functioning will allow a more holistic and integrative perspective of the habitat dynamics. The synergies between the increasingly available open‐access satellite images and cloud‐based platforms for planetary‐scale geospatial analysis offer an unprecedented opportunity to incorporate ecosystem processes and disturbances (such as fires, insect outbreaks or droughts) that have been so far largely neglected in ecological niche characterization and modelling. The most paradigmatic example of EFAs is the application of time series of spectral vegetation indices related to primary productivity and carbon cycle. EFAs related to surface energy balance and water cycles derived from remote sensing products such as land surface temperature or soil moisture enable a fine‐scale characterization of the species' niche—eventually improving the predictive performance of ENMs. All these advantages confirm that a new generation of ENMs based on such EFAs would offer great perspectives to increase our ability to monitor habitat suitability trends and population dynamics. However, despite the technical advances and increasing effort of remote sensing community to develop integrative EFAs, ENMs have yet to make full profit of the most recent developments by integrating them in ENMs. A coordinated agenda for remote sensing experts and ecological modellers will be essential over the coming years to bridge the gap between remote sensing and ecology disciplines and to take full (and timely) advantage of the fast‐growing body of Earth observation data and remote sensing technologies—with special emphasis on the development and testing of new variables related to key processes driving ecosystem functioning.

Current and future opportunities for satellite remote sensing to inform rewilding

“…It provides scientists and practitioners with a synoptic landscape perspective that is more difficult or expensive to achieve via other methods; this includes being able to track large‐scale disturbances such as floods and fires, and broad changes in vegetation cover at large spatial scales (Pettorelli et al., 2018b). Being able to access such information can enrich expert‐led or field data‐based assessments, for instance by providing information about the spatial distribution of indicators of habitat quality of key species (Regos et al., 2022).…”

Effects of input data sources on species distribution model predictions across species with different distributional ranges

“…Satellite remote sensing data from the Moderate Resolution Imaging Spectrometer (MODIS) on‐board the Terra satellite platform were used to derive remotely sensed ecosystem functional attributes (EFAs) (Regos et al, 2022, and references within), to characterize species habitat dynamics as a counterpoint/complement of climate data. To compute EFAs, we used the MODIS Enhanced Vegetation Index (EVI) (MOD13Q1.v006; 232 m pixel every 16 days) as a proxy of vegetation greenness, biomass and leaf area index—with values ranging from −1 to 1, with healthy vegetation generally holding values between 0.20 and 0.80, and for the 2000–2012 time period.…”

“…Imaging Spectrometer (MODIS) on-board the Terra satellite platform were used to derive remotely sensed ecosystem functional attributes (EFAs) (Regos et al, 2022, and…”

“…field‐sampled data, or interpolations from meteorological stations, derived from remote sensing, among others), and at different spatial and temporal scales (from broad extent/coarse grain to more local extent/fine grain) (Austin & Van Niel, 2011; Franklin et al, 2013), the uncertainty associated with variable selection should be carefully addressed (Varela et al, 2015; Waltari et al, 2014). For instance, while climate data have been traditionally used as predictors in SDMs to forecast changes in biodiversity, recent integration of satellite‐derived remote sensing data as predictors in SDMs offers some novel ecological insights (Arenas‐Castro & Sillero, 2021; Regos et al, 2022; and references within), but also generates additional uncertainty (Barsi et al, 2019; Borg et al, 2011).…”

MontObEO, Montesinho biodiversity observatory: an Earth observation tool for biodiversity conservation

“…Thus, the incorporation of human-related variables (e.g., Surface reflectance; SR-Band1) in the SRS-ENMs improves our capacities to monitor the impacts of human activities on biodiversity, enabling better-informed decision-making for conservation efforts 33 . Moreover, incorporating SRS products related to other environmental characteristics (i.e., soil properties or water detection and quality) can help to improve the accuracy of species habitat suitability predictions at fine scales, by enabling a more detailed characterization of the species' niche 34,35 . However, many species were not modelled due to the absence of distribution data: we modelled only 329 species out of a total of 1312 species.…”