Priority list of biodiversity metrics to observe from space

Abstract

Biodiversity encompasses the complex variety of life at all scales, ranging from genes to species to ecosystems. It encapsulates the structure, function, distribution, traits and composition of all living things. Crisis-level losses of biodiversity are stimulating action from local to global scales, as evidenced by establishment of the United Nations Sustainable Development Goals (SDGs) and Aichi targets and the current post-2020 negotiation of the Convention on Biological Diversity (CBD), as well as the first round of risk assessments by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) 1 . In response to these losses of biodiversity, the Group on Earth Observations Biodiversity Observation Network (GEO BON) 2,3 proposes a common framework of essential biodiversity variables (EBVs) 4 for monitoring biodiversity. These EBVs form a core set of complementary biological measurements for capturing considerable biodiversity change and are produced by

Satellite Remote Sensing and the Marine Biodiversity Observation Network: Current Science and Future Steps

“…Biodiversity is a key indicator of ecosystem health; changes in assemblages result in changing resilience, productivity, and interactions among species that can positively or negatively affect the ecosystem services that sustain human economies and social well-being (e.g., Worm et al, 2006;Duffy et al, 2013;Miloslavich et al, 2018). Satellite remote sensing is an important component of marine and terrestrial biodiversity studies (Muller-Karger et al, 2018a;Skidmore et al, 2021). It is a collection of methods that provides repeat synoptic coverage of ecosystems at multiple scales and with linkages between species-level information, such as distribution and richness, and between regional and global information, including primary productivity, nutrient cycling, and climate change (Turner et al, 2003(Turner et al, , 2015.…”

Analyzing the Relationship between Animal Diversity and the Remote Sensing Vegetation Parameters: The Case of Xinjiang, China

“…With the development of remote sensing technology, a series of remote sensing vegetation parameters, such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), the Leaf Area Index (LAI), and the Fractional Vegetation Cover (FVC), which can reflect the characteristics of the vegetation canopy structure, have been proposed [26][27][28]. Compared with the NDVI and EVI, these parameters can further reflect the habitat information for species [29,30], and long-time series quantitative remote sensing datasets based on these remote sensing vegetation parameters can provide an important opportunity to analyze the spatial distribution pattern of animal species richness caused by vegetation phenology changes on a large scale [31,32]. Nonetheless, the explicit analysis of the relationship between the animal species richness and the different vegetation phenology parameters extracted with remote sensing data is almost absent from the literature.…”

A remote sensing-based dataset to characterize the ecosystem functioning and functional diversity in the Biosphere Reserve of Sierra Nevada (SE Spain)

“…In addition, there have been calls for the use of ecosystem functioning variables to assess functional diversity at large scales to measure biosphere integrity (Mace et al, 2014;Steffen et al, 2015), one of the most challenging planetary boundaries to assess (Steffen et al, 2015). Despite the importance of ecosystem functioning variables and the conceptual frameworks developed to promote their use (Pettorelli et al, 2018), they have seldom been incorporated into 50 ecosystem monitoring (see Alcaraz-Segura et al, 2009;Fernández et al, 2010;Cabello et al, 2016;Skidmore et al, 2021).…”

“…Characterization and evaluation of ecosystem functioning can be carried out through functional traits or attributes related to the matter and energy exchanges between biota and the atmosphere (Box et al, 1989;Running et al, 2000). Nowadays, the use of satellite imagery provides useful methods to derive 55 such attributes, allowing for the spatially explicit characterization of ecosystem functioning and its heterogeneity (i.e., functional diversity) from local (Fernández et al, 2010) to regional (Alcaraz-Segura et al, 2006, and global scales (Ivits et al, 2013;Skidmore et al, 2021). Theoretical and empirical models support the relationship between spectral indices derived from satellite images (e.g., Enhanced Vegetation Index -EVI-) and essential functional variables of ecosystems, such as primary production, 60 evapotranspiration, surface temperature, or albedo (Pettorelli et al, 2005;Fernández et al, 2010;Lee et al, 2013).…”

“…The spatial reference system of all data is EPSG:4326 Datum WGS84. (Cabello et al, 2012;Pettorelli, 2016Pettorelli, , 2018 and management (Pelkey et al, 2003;Cabello et al, 2016), and for the study of biodiversity and ecosystems responses to environmental changes (Alcaraz-Segura et al, 2017;Pérez-Luque et al, 2015a, Skidmore et al, 2021. Recently, the use of EFAs derived from spectral vegetation indices in species distribution models has made possible to evaluate with high spatial and temporal precision habitat suitability for plant (Arenas-Castro et al, 2018) and animal species (Requena-Mullor et al, 2017;Regos et al, 2019) and may even anticipate expected changes in the distribution of threatened species as a consequence of climate change (Alcaraz-Segura et al, 2017).…”