Geodiversity in the Amazon drainage basin.

The Amazon is the largest drainage basin on Earth and contains a wide variety of abiotic landscape features. In spite of this, the geodiversity in this basin has not yet been objectively evaluated. We address this knowledge gap by combining a meta-analysis of an existing global geodiversity map and its components with a systematic literature review, to identify the key characteristics of geodiversity in the Amazon drainage basin (ADB). We also evaluate how these global geodiversity component maps, that are based on the geology, geomorphology, soils and hydrology, could be refined to better reflect geodiversity in the basin. Our review shows that geology-through lithological diversity and geological structures-and hydrology-through hydrological processes that influence geomorphology and soil diversity-are the main determinants of geodiversity. Based on these features, the ADB can be subdivided into three principal regions: (i) the Andean orogenic belt and western Amazon, (ii) the cratons and eastern Amazon, and (iii) the Solimões-Amazon river system. Additional methods to map geomorphological and hydrological diversity have been identified. Future research should focus on investigating the relationship between the geodiversity components and assess their relationship with biodiversity. Such knowledge can enhance conservation plans for the ADB. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Global geodiversity components are not equally represented in UNESCO Global Geoparks.

The aim of UNESCO Global Geoparks (UGGs) is to protect globally significant geoheritage and geodiversity, but quantitative evidence on the global representativeness of geodiversity components (i.e. geology, soils, geomorphology and hydrology) in these geoparks is in short supply. Here, we provide a first assessment by deriving a global map of geodiversity to test whether the presence of geodiversity components in UGGs is representative for the global availability and distribution of geodiversity. Using openly accessible global datasets and a newly developed workflow, we have calculated metrics for each geodiversity component and a global geodiversity index; we then quantified whether UGGs represent global geodiversity and then compared their components to a randomized spatial distribution of geoparks. Our results show that lithological and topographical diversity are more represented in UGGs than outside these sites, while soil type and hydrological diversity are not significantly different. Furthermore, individual soil types and lithological classes are under-represented and unevenly distributed in Asian and European UGGs. This is probably caused by the concentration of geoparks in Asian and European mountains. To better conserve geodiversity, we suggest an initiative to consider the protection and representation of all geodiversity components in their global context. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Towards a taxonomy of geodiversity.

Geodiversity is a topical concept in earth and environmental sciences. Geodiversity information is needed to conserve nature, use ecosystem services and achieve sustainable development goals. Despite the increasing demand for geodiversity data, there exists no comprehensive system for categorizing geodiversity. Here, we present a hierarchically structured taxonomy that is potentially applicable in mapping and quantifying geodiversity across different regions, environments and scales. In this taxonomy, the main components of geodiversity are geology, geomorphology, hydrology and pedology. We propose a six-level hierarchical system where the components of geodiversity are classified at progressively lower taxonomic levels based on their genesis, physical-chemical properties and morphology. This comprehensive taxonomy can be used to compile geodiversity information for scientific research and various applications of value to society and nature conservation. Ultimately, this hierarchical system is the first step towards developing a global geodiversity taxonomy. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Boundary of ecosystem services: A response to.

Chen et al. (2023) have proposed a scheme to define which services should be included as ecosystem services and which should be excluded so as to avoid "an all-encompassing metaphor that captures any benefit". We discuss the proposals, drawing attention in particular to definitions of 'natural capital' and 'ecosystems', the complexities of separating biotic from abiotic flows, and the importance of geodiversity and geosystem services in delivering societal benefits. We conclude that rather than trying to separate out bits of nature in order to draw the boundary of ecosystem services, it is perhaps time to avoid using 'nature' and 'biodiversity' as synonyms and think instead of a more holistic and integrated approach involving 'environmental', 'natural' or 'nature's services', in which the role of abiotic nature is fully recognised in both ecosystem services and non-ecosystem domains.

Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey.

The third Dutch national airborne laser scanning flight campaign (AHN3, Actueel Hoogtebestand Nederland) conducted between 2014 and 2019 during the leaf-off season (October-April) across the whole Netherlands provides a free and open-access, country-wide dataset with ∼700 billion points and a point density of ∼10(-20) points/m2. The AHN3 point cloud was obtained with Light Detection And Ranging (LiDAR) technology and contains for each point the x, y, z coordinates and additional characteristics (e.g. return number, intensity value, scan angle rank and GPS time). Moreover, the point cloud has been pre-processed by 'Rijkswaterstraat' (the executive agency of the Dutch Ministry of Infrastructure and Water Management), comes with a Digital Terrain Model (DTM) and a Digital Surface Model (DSM), and is delivered with a pre-classification of each point into one of six classes (0: Never Classified, 1: Unclassified, 2: Ground, 6: Building, 9: Water, 26: Reserved [bridges etc.]). However, no detailed information on vegetation structure is available from the AHN3 point cloud. We processed the AHN3 point cloud (∼16 TB uncompressed data volume) into 10 m resolution raster layers of ecosystem structure at a national extent, using a novel high-throughput workflow called 'Laserfarm' and a cluster of virtual machines with fast central processing units, high memory nodes and associated big data storage for managing the large amount of files. The raster layers (available as GeoTIFF files) capture 25 LiDAR metrics of vegetation structure, including ecosystem height (e.g. 95th percentiles of normalized z), ecosystem cover (e.g. pulse penetration ratio, canopy cover, and density of vegetation points within defined height layers), and ecosystem structural complexity (e.g. skewness and variability of vertical vegetation point distribution). The raster layers make use of the Dutch projected coordinate system (EPSG:28992 Amersfoort / RD New), are each ∼1 GB in size, and can be readily used by ecologists in a geographic information system (GIS) or analytical open-source software such as R and Python. Even though the class '1: Unclassified' mainly includes vegetation points, other objects such as cars, fences, and boats can also be present in this class, introducing potential biases in the derived data products. We therefore validated the raster layers of ecosystem structure using >180,000 hand-labelled LiDAR points in 100 randomly selected sample plots (10 m × 10 m each) across the Netherlands. Besides vegetation, objects such as boats, fences, and cars were identified in the sampled plots. However, the misclassification rate of vegetation points (i.e. non-vegetation points that were assumed to be vegetation) was low (∼0.05) and the accuracy of the 25 LiDAR metrics derived from the AHN3 point cloud was high (∼90%). To minimize existing inaccuracies in this country-wide data product (e.g. ships on water bodies, chimneys on roofs, or cars on roads that might be incorrectly used as vegetation points), we provide an additional mask that captures water bodies, buildings and roads generated from the Dutch cadaster dataset. This newly generated country-wide ecosystem structure data product provides new opportunities for ecology and biodiversity science, e.g. for mapping the 3D vegetation structure of a variety of ecosystems or for modelling biodiversity, species distributions, abundance and ecological niches of animals and their habitats.

Global raster dataset on historical coastline positions and shelf sea extents since the Last Glacial Maximum.

Motivation: Historical changes in sea level caused shifting coastlines that affected the distribution and evolution of marine and terrestrial biota. At the onset of the Last Glacial Maximum (LGM) 26 ka, sea levels were >130 m lower than at present, resulting in seaward-shifted coastlines and shallow shelf seas, with emerging land bridges leading to the isolation of marine biota and the connection of land-bridge islands to the continents. At the end of the last ice age, sea levels started to rise at unprecedented rates, leading to coastal retreat, drowning of land bridges and contraction of island areas. Although a growing number of studies take historical coastline dynamics into consideration, they are mostly based on past global sea-level stands and present-day water depths and neglect the influence of global geophysical changes on historical coastline positions. Here, we present a novel geophysically corrected global historical coastline position raster for the period from 26 ka to the present. This coastline raster allows, for the first time, calculation of global and regional coastline retreat rates and land loss rates. Additionally, we produced, per time step, 53 shelf sea rasters to present shelf sea positions and to calculate the shelf sea expansion rates. These metrics are essential to assess the role of isolation and connectivity in shaping marine and insular biodiversity patterns and evolutionary signatures within species and species assemblages. Main types of variables contained: The coastline age raster contains cells with ages in thousands of years before present (bp), representing the time since the coastline was positioned in the raster cells, for the period between 26 ka and the present. A total of 53 shelf sea rasters (sea levels <140 m) are presented, showing the extent of land (1), shelf sea (0) and deep sea (NULL) per time step of 0.5 kyr from 26 ka to the present. Spatial location and grain: The coastline age raster and shelf sea rasters have a global representation. The spatial resolution is scaled to 120 arcsec (0.333° × 0.333°), implying cells of c. 3,704 m around the equator, 3,207 m around the tropics (±30°) and 1,853 m in the temperate zone (±60°). Time period and temporal resolution: The coastline age raster shows the age of coastline positions since the onset of the LGM 26 ka, with time steps of 0.5 kyr. The 53 shelf sea rasters show, for each time step of 0.5 kyr, the position of the shelf seas (seas shallower than 140 m) and the extent of land. Level of measurement: Both the coastline age raster and the 53 shelf sea rasters are provided as TIFF files with spatial reference system WGS84 (SRID 4326). The values of the coastline age raster per grid cell correspond to the most recent coastline position (in steps of 0.5 kyr). Values range from 0 (0 ka, i.e., present day) to 260 (26 ka) in bins of 5 (0.5 kyr). A value of "no data" is ascribed to pixels that have remained below sea level since 26 ka. Software format: All data processing was done using the R programming language.