Remotely Sensed Variables of Ecosystem Functioning Support Robust Predictions of Abundance Patterns for Rare Species

Abstract

Global environmental changes are affecting both the distribution and abundance of species at an unprecedented rate. To assess these effects, species distribution models (SDMs) have been greatly developed over the last decades, while species abundance models (SAMs) have generally received less attention even though these models provide essential information for conservation management. With population abundance defined as an essential biodiversity variable (EBV), SAMs could offer spatially explicit predictions of species abundance across space and time. Satellite-derived ecosystem functioning attributes (EFAs) are known to inform on processes controlling species distribution, but they have not been tested as predictors of species abundance. In this study, we assessed the usefulness of SAMs calibrated with EFAs (as process-related variables) to predict local abundance patterns for a rare and threatened species (the narrow Iberian endemic ‘Gerês lily’ Iris boissieri; protected under the European Union Habitats Directive), and to project inter-annual fluctuations of predicted abundance. We compared the predictive accuracy of SAMs calibrated with climate (CLI), topography (DEM), land cover (LCC), EFAs, and combinations of these. Models fitted only with EFAs explained the greatest variance in species abundance, compared to models based only on CLI, DEM, or LCC variables. The combination of EFAs and topography slightly increased model performance. Predictions of the inter-annual dynamics of species abundance were related to inter-annual fluctuations in climate, which holds important implications for tracking global change effects on species abundance. This study underlines the potential of EFAs as robust predictors of biodiversity change through population size trends. The combination of EFA-based SAMs and SDMs would provide an essential toolkit for species monitoring programs.

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

“…Sentinel-2 is an open-access resource that provides spatial data at a resolution useful and necessary for this task, and, despite its relatively recent release, effective long-term ecosystem monitoring at local, regional, and national levels is planned to be continuously ensured by this satellite. As such, considering the increasing Sentinel-2 temporal span, future studies on conservation planning incorporating information for longer periods, as it is actually done with other satellites [104], may be valuable because they more likely minimize the common pitfall of assuming stable environmental suitability, and therefore populations persistence, over time [105,106].…”

No place to hide: Rare plant detection through remote sensing

“…Studies using RS to focus specifically on rare plants remain uncommon, although their number has been growing in recent years (e.g. Arenas‐Castro et al., 2019; Gonçalves et al., 2016; Zhu et al., 2016). As an emerging research field, the potential benefits of RS for the detection of rare plants remain unclear.…”

A Framework for Multi-Dimensional Assessment of Wildfire Disturbance Severity from Remotely Sensed Ecosystem Functioning Attributes

“…These include frequently used satellite-derived descriptors of the intra-annual dynamics of carbon gains, such as primary productivity, vegetation seasonality, and phenology (e.g., [46,52,53]). However, analogous measures can also be extracted from RS-derived proxy descriptors related to other dimensions of ecosystem functioning, such as water content, albedo, and sensible heat (e.g., [50,54]). By combining these four complementary dimensions of ecosystem functioning across each component (i.e., quantity, seasonality, and timing), remotely sensed EFAs can enable indepth and integrative characterizations of fire severity [3,44].…”