Sequencing to satellites: Integrating biodiversity surveys across scales
Creators: name: Slater, Andrew
Publisher: name: Lancaster University
Year: 2025

description: The global decline of natural ecosystems is a consequence of a number of
 phenomena, including climate change, habitat loss and fragmentation, and
 the introduction of invasive alien species. As a result, the ecosystem
 services provided, including carbon sequestration, nutrient cycling
 through the trophic pyramid, and pollination and seed dispersal, are at
 risk. Mitigation strategies to halt and reverse these trends can be
 devised but must be informed by spatially and temporally accurate
 information on the location and causes of ecosystem change. This type of
 scalable ecological modelling directly supports global biodiversity
 frameworks, such as the Kunming-Montreal Global Biodiversity Framework
 (GBF), which call for spatially explicit monitoring of ecosystem condition
 and change.
 
 
 While field surveys can provide accurate information to inform mitigation
 strategies for target ecosystems, they are often costly in terms of time
 and resources. Consequently, the frequency and spatial and temporal extent
 at which field surveys can be conducted is limited. In contrast, Earth
 observation (EO) satellites provide freely available, repeated,
 wall-to-wall information of the Earth’s surface. The combination of EO
 data with environmental data has been successfully employed to describe
 land classes, and quantify changes within them, such as levels of
 deforestation. Although the description of land classes enables some
 inference of expected ecosystem structure, ecosystems do not always
 transition categorically, although abrupt land use change can result in
 clear shifts. More often, ecosystems vary gradually across landscapes,
 much like the reflectance values recorded by EO satellites. It is
 hypothesised that the composition of the studied communities will vary in
 accordance with the structure of the habitat, and that elements of this
 structure will be described by EO.
 
 
 The objective of this thesis was to investigate the potential of combining
 EO data with field survey data in multi-species distribution and occupancy
 models to predict community composition across landscapes. This approach
 enables the investigation of the influence of environmental factors on
 biodiversity and the inference of ecosystem condition. The study used four
 distinct groups of field data: plant communities in the Succulent Karoo of
 South Africa, insect communities in the vicinity of the Gola Rainforest in
 Sierra Leone, and bird communities identified in the Peruvian and north
 Brazilian Amazon. These data sets comprised between 120 and 235 species,
 which were included in analysis. 
 
 
 The study found that for all data sets, models fitted with EO data yielded
 predictions of community composition that were as or more accurate than
 those made by models fitted with environmental data. In predicting the
 validation data, the community mean area under the curve (AUC) values
 ranged between 0.58 and 0.66. The models generated AUC’s exceeding 0.7 for
 between 21% and 49% of the individual species modelled. These values
 represent a moderate predictive performance overall, with the accuracy of
 models influenced by the strength and extent of ecological gradients,
 species detectability, and survey design.
 
 
 The findings indicate that there are consistent associations between EO
 variables and a large number of species from diverse taxonomic groups and
 geographical locations. It can be reasonably assumed that a significantly
 higher proportion of taxa could be predicted using similar methodologies
 provided that sufficient field surveys are conducted to calibrate similar
 models. This highlights the importance of obtaining high-quality
 ecological data through field surveys in order to calibrate our
 interpretation of ecological changes observed through remote sensing. The
 ability to apply EO data retrospectively and prospectively also offers a
 valuable opportunity for long-term biodiversity monitoring across changing
 landscapes.