Global biodiversity monitoring: From data sources to Essential Biodiversity Variables

Abstract

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance Manuscript spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplank-ton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the shortwave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radio-metric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

The Diversity-Weighted Living Planet Index: Controlling for Taxonomic Bias in a Global Biodiversity Indicator

“…While the geographic and taxonomic bias we demonstrate in the LPI is consistent with other studies [8,33] and comparable data sets [21], the spatial mismatch between the known diversity of vertebrate species and the available data (Fig 2) could lead to inaccurate estimates of status and trends in biodiversity. More specifically, trends that equally weight these species groups (as in the ‘traditional’ Living Planet Index) will be significantly biased by the disproportionate representation of these groups, skewing the calculation of trends in global wildlife abundance.…”

A comparison of macroecological and stacked species distribution models to predict future global terrestrial vertebrate richness

“…EBVs function as an interface between raw data and indicators and are meant to provide robust and coordinated data about biodiversity change on a global scale in order to inform policy makers (Brummitt et al, 2017;Geijzendorffer et al, 2016). Field studies and monitoring schemes provide data for a wide range of EBVs, but are often limited in spatial or temporal coverage, whereas large-scale data sources, such as the Global Biodiversity Information Facility (GBIF), are inherently biased (Meyer, Kreft, Guralnick, & Jetz, 2015) and not representative on a global scale (Proença et al, 2017). Field studies and monitoring schemes provide data for a wide range of EBVs, but are often limited in spatial or temporal coverage, whereas large-scale data sources, such as the Global Biodiversity Information Facility (GBIF), are inherently biased (Meyer, Kreft, Guralnick, & Jetz, 2015) and not representative on a global scale (Proença et al, 2017).…”

Evolución del riesgo de extinción y estado actual de conservación de las aves de Colombia

“…Por ejemplo, el indicador de "Planeta Vivo" (Living Planet Index, LPI), creado por el Fondo Mundial para la Naturaleza (Wild Wildlife Fund, WWF) en 1998, se utiliza para monitorizar las tendencias en la biodiversidad. Mide las tendencias poblacionales usando datos de series de tiempo para calcular tasas de cambio en las poblaciones de especies silvestres de vertebrados terrestres, marinos y dulceacuícolas (Loh & Wackernagel, 2004, Loh, et al, 2005 y es considerado como uno de los conjuntos de datos más completos de observaciones sobre la abundancia de las poblaciones (Proença, et al, 2017). Al igual que los libros rojos, el LPI transmite de una forma sencilla al público no experto (como los responsables de decisiones, los políticos, el público general), la información sobre las tendencias de la biodiversidad .…”