In this research field, we develop data, modelling frameworks and informatics solutions to address current limitations in global knowledge of biodiversity and ecosystem dynamics that hamper environmental research, monitoring, and other fields of application.
Current projects contributing to this research field:
Fine-scale mapping of global dynamics in biodiversity habitat
The loss, fragmentation, and degradation of natural habitats is a major driver of change in biodiversity and loss of ecosystem services worldwide. Under land-use change, climate change, and other increased anthropogenic pressures, these phenomena are believed to accelerate further and cause extensive future losses of ecosystem integrity. Mapping and monitoring the spatial and temporal variability of habitats is essential to anticipate and address these environmental problems.
In this project, we are developing a global gridded data cube of annual area-of-habitat maps at high-resolution that comprehensively capture the dynamics of multiple standardized habitat types over the past decades. These maps offer a sufficiently high spatial and thematic detail to allow for biodiversity and ecosystem services applications. By developing spatially explicit information on habitat dynamics, we can help practitioners identify endangered species, identify the drivers that threaten them, and decide on effective interventions.
Project lead: Ruben Remelgado
Work in progress
We are currently finalizing the development of the global data cube of annual area-of-habitat maps with 1km resolution. The data cube will be composed of 63 habitat types and cover a period of 24 years between 1992 and 2015. The area-of-habitat maps will conform to the class scheme of the Red List of Threatened Species assessment developed by The International Union for Conservation of Nature (IUCN), assuring its interoperability with ongoing assessments of the habitat preferences of thousands of species. To achieve this, we utilized hundreds of terabytes of environmental datasets derived with petabytes of satellite imagery, bringing together decades of efforts in mapping land surface dynamics.
We also combined this data cube with species-specific distribution and ecological information to map how the captured habitat dynamics affect biodiversity and ecosystem services.
Improving alien species distribution knowledge by integrating data across scales
Species have been transported by humans across the planet both intentionally and unintentionally. Occasionally, they are able to establish self-sustaining populations in the introduced regions, becoming naturalized and potentially affecting local biota. Several studies have pointed to loss in biodiversity and ecosystem degradation due to species invasion. Detailed knowledge on introduced species distributions is needed in order to identify and address the threats imposed by them. However, information about species distributions is lacking for most taxa, and, when available, is typically incomplete, biased and measured at disparate scales.
This project advances knowledge of global alien species distributions. It identifies and quantifies biases, gaps and uncertainties in different data sources on alien species. Using alien species as a model system, it also develops general protocols to facilitate the interpretation and robust use of different types of species distribution data by scientists, resource managers, and policy-makers.
Project lead: Eduardo Arlé
Work in progress
Understanding species distributions is critical for addressing manifold ecological questions, but available data are typically highly heterogeneous and rife with various information gaps and uncertainties. Crucially, the accuracy and biogeographical status (native vs. alien) of individual data points is often unclear, thus challenging their confident use in distribution modelling or other downstream analyses. We developed a framework, currently being implemented as an R package called bRacatus, for estimating the accuracy and biogeographical status of a given occurrence record.
We developed and tested our methods based on terrestrial species data representing four taxa. The estimation is based on the spatial context provided by coarser-grain reference regions of native and/or alien distributions. By estimating records’ likelihoods along two axes, i) “certainly false to certainly true”, and ii) “certainly native to certainly alien”, the framework avoids the artificial thresholds of simplistic data filtering and instead allows propagating uncertainties in subsequent analyses. This package provides support for researchers working with SDMs, biogeographical patterns and other analyses that rely on point-record data. We will make the package available GitHub very soon.
Uncertainties in global land-use and land-cover datasets
Detailed information on global land use and land cover is crucial for studying climate change, biodiversity loss, food security, and many other contemporary issues. The integration of heterogeneous data sources through advanced statistical and computational tools opens exciting avenues for mapping land-use dynamics globally at fine resolutions. However, due to widespread but unknown gaps and uncertainties in primary observational data, the derived gridded maps inherit tremendous biases that challenge their downstream application.
In this project, we aim to advance the validation and continuous quality-improvement of global land-use and land-cover data. We will detect, quantify, map, and ultimately reduce uncertainties in various sources of primary data on land-use and land-cover changes, such as remotely sensed environmental data, agricultural and forestry census data, and in-situ point observations. We will also assemble an open database with ground-truthing information from existing databases and through a data validation campaign in the citizen-science platform Geo-WIKI.
This project will support the development of a next generation of uncertainty-conscious gridded data-products, including those developed in the MAS group for agricultural and forestry systems and biodiversity habitats. Moreover, the results from this project will enable targeted efforts to close gaps in global primary information on land-use and land-cover changes.
This project also contributes to the development of standardized vocabularies and best practices for land-use data and data-quality assurance. The mobilized data and derived maps will be published using FAIR principles.
Project lead: Caterina Barrasso
Shedding lights on land-use and biodiversity changes in former USSR and Arabic-speaking countries
Documenting global land-use dynamics and their ecological consequences are central topics in environmental change and sustainability. However, global scientific databases documenting these dynamics are biased by vast data gaps across Asian former-USSR and Middle-Eastern countries. Many datasets indeed exist for these countries, but remain hidden from the eyes of ‘global’ scientific assessments in Russian- and Arabic-language literature and databases. A key reason are persisting intercultural, communication, and collaboration barriers between scientists and practitioners from 'Western' and non-'Western' countries.
This project addresses this long-standing data divide. Led by two scientists with backgrounds in former-USSR and Arabic-speaking countries, and collaborating with regional partners, we will establish contacts to data holding institutions and scientists. Through these contacts, we will seek access to offline databases and non-digital sources in libraries of universities, NGOs, statistical offices, and other institutions. In parallel, we will perform comprehensive online searches for datasets published in Russian- and Arabic-language scientific and grey literature.
We will harmonize and integrate all mobilized databases and newly digitized data into global scientific databases using FAIR principles. Focusing on selected areas in Central Asia, the Caucasus, Siberia, North Africa and the Middle East, we hope to develop a wide collaborative network across these regions.
Project lead: Dr Abdualmaged Abdulraqeeb Alhemiary
Project lead: Evgeniya Elkina
As the result, global gridded land-use datasets inherit tremendous geographical biases and uncertainties in these parts of the world, and the ecological consequences of regional land-use changes remain largely unquantified. These regional gaps may also bias our understanding of global patterns and driver-response relationships in human-environment systems, thus misguiding science-based strategies to advance sustainable development.
Limitations in global information on species occurrences