Summary 

In the Netherlands, collective farmer contracting to make biodiversity and habitat conservation on a landscape level more ecologically efficient, has long been implemented. Multidimensional data cubes can be a great vehicle to integrate individual data across farmlands and monitor conservation efforts on a landscape level. A landscape level approach is crucial for conservation of biodiversity and habitats as farmland birds and other species don’t adhere to the individual farmland level, hence their conservation requires cooperation across farmers.

Needed skills 

Python, EBVs, biodiversity monitoring experience in agricultural setting, handling and processing ecological long tail data, species distributions.

Summary

In this project, we aim to create a simulation framework for biodiversity data cubes using the R programming language. This can start from simulating multiple species distributed in a landscape over a temporal scope. In a second phase, the simulation of a variety of observation processes and effort can generate actual occurrence datasets. Based on their (simulated) spatial uncertainty, occurrences can then be designated to a grid to form a data cube. Nevertheless, we encourage you to think out of the box and provide new ideas as well. For the occurrence-to-cube designation, R code is already available.

Needed skills

R programming; Algorithm development; Creative thinking; Problem solving; Efficient coding; Collaboration (Git).

Summary

For the finance and insurance sectors to benefit from opportunities provided by nature and mitigate nature-related risks, reliable biodiversity monitoring data is a must. To encompass the many dimensions of nature and service the Financial Services market, NatureMetrics developed the NatureMetrics digital dashboard which combines biodiversity footprint, biodiversity impact and biodiversity trends metrics. Thus, this framework concomitantly measures biodiversity pressures and responses. This project proposes the incorporation of standardised biodiversity data from B-Cubed within iNPI, for example, during design of open-source connectivity algorithms at high spatial resolution to link with the framework’s restoration tracker element, to maximise the efficiency and return of investment of biodiversity management interventions, and demonstrate the high-resolution required for the Financial Services market. 

Needed skills

Data scientists; Bioinformaticians; Statistical ecologists; Mathematical modelling; Machine learning.

Summary

This project explores the added value of biodiversity data cubes to identify critical habitats using Deep-Learning Species Distribution Models (Deep-SDMs). The conventional Deep-SDM approach often relies on observation point data, which may struggle to capture the dynamic temporal evolution patterns of species distributions. The integration of biodiversity data cubes with environmental and remote sensing data will be performed to improve Deep-SDMs and thus better describe habitats. To illustrate this approach, we will explore a use case based on floristic data from France and Belgium. Using detailed floristic data from these two countries, we will demonstrate how the integration of data cubes can contribute to the modelling of species distribution over an extended geographical scale, taking into account environmental and remote sensing data. This case study will serve to highlight the potential benefits of using data cubes in biodiversity modelling.

Needed skills

Knowledge of GBIF data and data cube extraction, data-viz and machine/deep learning algorithms.

Summary

This project will funnel relevant biodiversity databases such as OBIS, GBIF or ETN, and environmental data sources such as Copernicus Marine Service or Bio-Oracle, into a Spatial Temporal Asset Catalog (STAC). This OGC-compliant specification aligns with the GBIF API. The STAC back-end will serve as an open-access entry point of ecological modelling-ready data cubes following the B-Cubed software specification. These cubes will be compatible for plugging into machine learning and modelling workflows. The project will showcase its potential for early risk assessment of non-indigenous species invasions with SDMs to calculate the probability of potential invasive species to colonise an area. It will build upon existing tools and develop new ones using well-known data science programming languages such as python or R, allowing reuse for other marine scientists, policy-makers and the wider community.

Needed skills

Policy and communication.

Summary

Spectral diversity serves as a measure for vegetation and trait diversity of plant communities and their geological surroundings. From a Critical Zone perspective, it considers plant communities’ trait expression, accounting for taxonomic composition and abiotic factors. Emphasising trait response to abiotic factors changes requires considering the temporal dimension and taking a phenological stance. The R package rasterdiv facilitates spectral diversity analysis, in particular with Rao'Q. It enables dealing with multidimensional traits, assuming they are uncorrelated which is not the case for vegetation index time series. This project will implement a method in rasterdiv, allowing it to deal with times series, opening the possibility to correctly use phenological data to explore plant trait biodiversity.

Needed skills

DTW libraries, Phenology Data, algorithms, and resources to obtain them; Rasterdiv package knowledge; Scripting capacity in R/python; Knowledge of use case data & community ecology.

Summary

The project, part of the doctoral thesis "Improving Semantic Interoperability of observational research through data FAIRification," focuses on representing eLTER Standard Observations (SOs) for eLTER PLUS (871128). The SOs will include the minimal variables set and associated method protocols to characterise Earth systems’ state and future trends. The project will semantically enhance variable descriptions in EnvThes with the I-ADOPT Framework, enabling semantic precision and machine-readability. For the B-Cubed hackathon, the project targets biodiversity variables, including flying insects, vegetation composition and occurrence data of birds using acoustic recording observed on the LTER Site “Zöbelboden”. Building on existing specifications like the Species occurrence cube, the project aims to contribute to FAIR, semantically rich representations, ensuring interoperability beyond eLTER requirements.

Needed skills

FAIR data management; Data modelling; Knowledge engineering; Semantic modelling; FAIR metadata schema development; Jupyter Notebook & Python.

Summary

In most cases, there is no complete information about the ‘reality’ of the focal species distribution besides the data collected in-situ. This is partly because the completeness of the data extracted from surveys (recorded in-situ) is difficult to measure. This affects our ability to detect the real spatial coverage as well as the niche of a species with the records available. These limitations, in turn, can seriously flaw final results of species distribution models, by distorting the relationship between species occurrences and the underlying environmental patterns. Quantifying error sources is crucial for effective modelling, especially in conservation efforts. Under this scenario, using simulated or in-silico datasets — the so-called ‘virtual ecologist’ approach — allows to generate distribution data with known ecological characteristics, helping to simulate and thus account for spatio-temporal and taxonomic noises, thanks to the complete control on the configuration of factors constraining the distribution of species.

Needed skills

Knowledge of R; Programming/modelling; Ecological/conservation skills.

Summary

Species occurrence cubes will be available in a limited range of formats, which may hinder the reuse of these cubes in the future.  This project aims to increase the range of formats that species occurrence cubes will be available and make them as ready-for-use as possible.     

Needed skills

Knowledge of existing conversion tools and/or scripting using libraries using common programming languages such as Python, C, Node, Java etc. The choice of tools or scripting language is open for participants to choose – the more the merrier! 

Summary: Traditional biodiversity monitoring programmes usually hold well-curated data of species occurrences, with high spatial and temporal resolutions but often only for restricted areas. In recent years, technological developments along with online digital platforms have increased the amount of information available on species occurrences, both from the scientific community as well as from community-based contributions such as citizen science. The increase in data availability and accessibility poses the challenge of integrating such highly heterogenous data sets on species occurrences, but in the context of the implementation of biodiversity policy objectives at wide scales, making the most of all information available is crucial. While integration of species occurrences from different data sources cannot be discarded, we can expect that variability in sampling methods, spatial and temporal resolution and coverage, as well as data confidence levels, will introduce bias in the assessments. 

Needed skills: Programming, EBVs particularly on species distributions, biodiversity monitoring experience, indicators development and use.

Summary: Biodiversity indicators provide essential information for policy-makers, but often are only accessible to those with an understanding of coding and the time to learn a new package, namely research scientists. That means many users must rely on ready-made or commissioned analyses. Within the EU-project B-Cubed, we are providing flexible workflows to align with policy objectives. But to be truly user-friendly, these should be provided in graphical, interactive format that does not require the user to understanding coding. The goal of this hackathon project is to develop a front end for a general biodiversity indicators package e.g. in the form of a Shiny app.

Needed skills: Shiny and R, ideally with a bit of S3 knowledge.