A new, reproducible R workflow that enables ecologists to standardise the measurement of individual differences in habitat use based on GPS data, demonstrated using data on 13 lapwings, has been published in Individual-Based Ecology.
The ecological niche concept describes the set of environmental conditions a species needs to survive and reproduce, but it has traditionally been applied at the species or population level – even though individual animals are known to vary widely in behavior, diet, and habitat choice.
Individuals belonging to the same species do not necessarily use or respond to their environment in the same way. This variation has been traditionally treated as statistical noise but now we know that it has important implications for the long-term survival of a species.
said Dr. Takola
Despite this, few tools exist to translate modern tracking data and space use patterns into practical, comparable measures of individual habitat specialisation. This new workflow aims to address this gap.
Heuristic representation of the different niche levels. A) A community can be represented as a set of species in which each occupies a different niche. B) A species can be represented as a set of metapopulations with different niches. C) A population consists of multiple individuals with different individualised niches (the potential niche is shown with transparent dots, and the realised niche is shown with bold dots). Adapted from Takola and Schielzeth (2022).
Working at the individual level, it treats each animal’s personal range of habitat conditions as its own ‘individualised niche’, distinguishing for each individual between the conditions it actually uses (its realised niche) and the conditions available to it but not necessarily used (its potential niche).
These concepts were defined in a previous study by E. Takola and H. Schielzeth. Takola has now taken these concepts a step further, translating them into a practical workflow for studying individual animals in the wild.
To achieve this, it uses combined mixed-effects resource selection functions such as statistical models that estimate both average habitat preferences and how much individuals deviate from that average with hypervolume exploration methods, a way of mapping all the environmental conditions an individual could occupy as a multidimensional space.
Graphical abstract of the workflow. Generated by Dr. Takola using ChatGPT.
Implemented entirely in R, the workflow draws on tools from ecological niche modeling, behavioral ecology, and spatial ecology. Further, the workflow is divided into three stages: data preparation, analysis, and output generation.
Throughout, it distinguishes between the environmental conditions available to an individual and the conditions it actually uses, allowing researchers to quantify niche breadth or how broad or narrow an individual’s habitat use is; niche overlap or how much individuals’ habitat use overlaps with one another; and repeatability – how consistent an individual’s habitat use is over time.
The lapwing case study puts this into practice. By using publicly available GPS tracking data from 13 northern lapwings, the study shows how the workflow integrates multiple environmental layers such as earthworm abundance, human presence, pesticides, management, soil variables, and vegetation.
Why Individual Variation Matters And Future Outlook
Individual organisms differ in genotype, morphology, life strategy, diet, and behavior. Individual-based approaches capture how animals adapt locally and respond to environmental stress in ways population-level averages can obscure.
This has real implications for conservation. Individual-based models, ones that account for energy costs, demographic trends, habitat-selection patterns, and life-history traits, offer a more accurate, mechanistic picture of wild population dynamics, and a better basis for predicting how populations will fare as conditions change.
Although demonstrated on the northern lapwing, the workflow is designed to be transferable to other mobile species with GPS tracking data, giving ecologists a general-purpose tool for incorporating individual variation into habitat and conservation models.
explains Takola
The data and code underpinning the workflow are available on GitHub.
By making individual specialisation measurable and comparable across taxa and datasets, the workflow can help researchers study niche specialisation and population-level heterogeneity across ecological scales, linking individual-level variation back to population- and species-level patterns, and giving conservationists a tool to move beyond population averages toward more targeted conservation methods.
Original source:
Takola E (2026) The individualized niche in motion: Quantifying individual specialisation with movement data. Individual-based Ecology 2: e203247. https://doi.org/10.3897/ibe.2.203247
By combining and improving digitally available data and models, BioDT offers approaches for sustainable biodiversity management and ecosystem conservation.
Biodiversity is essential for the processes that support all life on Earth. It provides critical resources such as food and energy, and supports ecosystem health. However, climate change, deforestation, and pollution are destroying habitats, altering ecosystems, and eliminating – or introducing – species that are fundamental for planet’s biosphere.
Cover of the “Building Biodiversity Digital Twins” article collection in RIO journal.
The BioDT project aims to revolutionise our understanding of biodiversity dynamics by integrating advanced modelling, simulation, and prediction capabilities. By combining and improving digitally available data and models, BioDT offers approaches for sustainable biodiversity management and ecosystem conservation. BioDT’s combines expertise in biodiversity, ecological modelling, FAIR data, high-performance computing, and artificial intelligence.
BioDT aims to enhance the accuracy and predictive performance of biodiversity models through iterative development and validation against independent data. This approach can be critical for developing decision support tools and policy development. By continuously updating data, BioDT will provide real-time predictions of biodiversity patterns and processes through interactive maps and summaries. The consortium leverages existing technologies and data from major research infrastructures (GBIF, eLTER, DiSSCo, and LifeWatch ERIC) to achieve this goal.
A screenshot of the BioDT homepage.
The project’s impact extends to addressing critical issues, including impact of environmental change on species and ecosystems, food security, and the implementation of the EU and international policies. The project contributes to the UN Sustainable Development Goals 2 (Zero Hunger), 3 (Good Health and Well-being), 13 (Climate Action), and 15 (Life on Land).
BioDT develops prototype Digital Twins for biodiversity conservation
In order to test its modelling system, BioDT is developing ten prototype digital twins (pDTs) focused on species and ecosystems of high conservation and policy concern, such as invasive species, pollinators and grasslands. The pDTs are divided into four main groups:
Species Response to Environmental Change: focus on the interactions between species and ecosystems. By incorporating temporal dynamics rather than pure space-for-time substitutions, BioDT improves temporal predictions and accuracy. Different sources of uncertainty are quantified using extensive geographic data combined with high-resolution time-series data in a single modelling framework.
Genetically Detected Biodiversity: addressing food security and challenging environments by integrating genomic methods based on DNA data with traditional biodiversity data. These twins focus on crop wild relatives and other genetic resources for farming and food security, as well as DNA-detected biodiversity in poorly known habitats.
Dynamics of Species of Policy Concern: applying modelling and high-performance computing to invasive and alien species recognised at EU and national levels. This twin involves using current species occurrence data, and tackling crucial environmental conditions and invasive effects on native taxa and ecosystems.
Influence of Species Interactions: predicting disease outbreaks using vector species and exploring the patterns and processes of insect pollinators. Work on interaction twins involves further development of data exchange models and establishing temporal historic reference points through digitisation of collection specimens.
A screenshot from the BioDT homepage showing the purposes of prototype digital twins.
The pDTs aim to make essential datasets, best practices, expertise, and lessons learned available and ready for use to researchers and research infrastructures in implementing the use cases, while providing.
The pDTs test the models predictive performance and data availability scenarios, and apply them to address biodiversity challenges through scenario simulations, predictions, and biomonitoring methods. This iterative approach aims to integrate and compare the predictive performance of various modelling approaches, stimulating the development of next-generation prototypes.
Building Biodiversity Digital Twins: a BioDT collection of scientific papers
To further advance the development and reliability of Biodiversity Digital Twins, the BioDT team has produced 10 scientific papers, compiled in the “Building Biodiversity Digital Twins” issue of the open-science scholarly journal Research Ideas and Outcomes (RIO).
“The collection offers an in-depth understanding of the conceptual and technical advancements achieved towards developing digital twins for a wide range of biodiversity topics. Through the BioDT project, we are enabling a broad audience to interactively understand and predict biodiversity changes across space and time.” says Gabriela Zuquim, Scientific Coordinator at CSC for the BioDT project
The collection serves as a centralised access point to project outputs by the BioDT initiative. Publication of rather unconventional and not traditionally published research outputs is in fact amongst the unique features of the open-science RIO journal. Another feature is the possibility of individual publications to be mapped to the SDGs they contribute to, thereby further underlining their significance.
A conceptual diagram of a digital twin prototype from this paper. The core aim of this project is to test the feasibility of generating essentially real-time updating predictions on bird spatiotemporal distributions and singing activity by combining prior information, based on long-term monitoring data with continuously accumulating new information provided by citizen scientists.
In the case of BioDT, RIO has made it possible for the project team to illustrate the process of prototyping Biodiversity Digital Twins in the format of a peer-reviewed scientific article, thereby ensuring its discoverability, credibility, citability, reusability and long-term public availability. By opting for this transparent approach to sharing their scientific work that has standed the rigour of formal scientific review, the BioDT project ensures that future scientists can make better and more efficient use of the models developed by the consortium’s researchers, data, and cutting-edge technology.
For example, one publication describes the HONEYBEE Prototype Digital Twin. The prototype will allow, after the ongoing calibration with land use and hive weight data, predictions of honeybee population dynamics, mite infestation and honey production. The model was developed based on a previously developed one, devised to simulate foraging of a single bee colony. By using the prototype digital twin, users can interactively apply the model on various time and geographic scales ranging from local sites to whole regions or even country level. Thus, it can become an essential tool for the assessment of the viability and productivity of honey bee colonies around Germany, regardless of the specificity of landscapes and management strategies.
Overview of the prototype HONEYBEE-pDT
Our vision is that the assessment can even be run to take into account different climate-change scenarios. The publication also provides guidelines to potential users of the prototype. The authors of the paper, led by Dr Jürgen Groeneveld (Helmholtz Centre for Environmental Research – UFZ, Germany) reminds that despite honey bees “being a managed species, they are severely affected by climate change, emerging parasites and diseases, modern agricultural land use and possibly inappropriate beekeeping practices”, while going on to cite worrying data about the trends in both Europe and the USA.
Similarly, other publications already available from the collection address equally crucial and pressing issues with impact on a global scale, including disease outbreaks, crop management, invasive species, bird and vegetation dynamics.
“The Building Biodiversity Digital Twins collection of project papers suited our needs perfectly,” said Dmitry Schigel, GBIF Scientific officer and a coordinating editor of the collection. “The project team agreed to capture the project’s iterations and reveal our two-thirds stage prototypes two years into the project with one more to go. The innovative platform that the Pensoft’s RIO journal provides lets us describe our progress in a less formal but still peer-reviewed setting. Thanks to the efficient work of the author teams, reviewers and co-editors, this special issue came together quickly and now enables our prototype digital twin teams to attract and process feedback from broader audiences”
Invasive plant species pose a major threat to biodiversity and ecosystem health worldwide. However, predicting the exact impact of these invasions is challenging due to the complexity of interactions between invading species, native communities, and impacted ecosystems.
Outlined in a study published in the open-access journal NeoBiota, the framework combines new technologies and techniques to learn and predict how invasive plants alter ecosystems over time and in different environments.
Animated model visualisation of spatial-temporal dynamics of invader impacts. Click here to download a detailed explanation of the model.
The new framework integrates several modern advancements:
Environmental mapping: Progress in remote sensing and ecological monitoring allow researchers to capture detailed information about the environmental conditions of invaded areas. Drones, satellites, and advanced sensory networks can be used to create detailed ecosystem maps, which show how invasive species interact with their environment.
Functional tracers: These are specific indicators that reflect changes in ecosystem functions caused by invasive species. For example, researchers can track the impact of nitrogen-fixing invasive plants on ecosystems using nitrogen isotopes.
Spatio-temporal modelling: By combining environmental data with new modelling techniques, such as AI, researchers can create detailed models showing the spread and impact of invasive species on ecosystems over time. Such models can predict how changes in environmental conditions, such as climate change, might influence an invasive species’ success.
Mechanisms determining plant invasion impact.
Beyond scientific analysis, novel technologies also facilitate communication of ecological impacts, as the authors demonstrate in an animated 3D-video visualisation.
“The framework we’ve introduced offers researchers deeper insights into how invasive plant species interact with their environments, enabling more targeted management to lessen their ecological impact. We advocate for stronger collaboration between ecologists and technical experts to refine and expand these methods.
“Going forward, further research and integration of the wide range of recent methods and tools are needed to enhance the framework’s effectiveness.”
The research team behind the new framework: Christiane Werner, Christine Hellmann and André Große-Stoltenberg.
Original source
Werner C, Hellmann C, Große-Stoltenberg A (2024) An integrative framework to assess the spatio-temporal impact of plant invasion on ecosystem functioning. NeoBiota 94: 225-242. https://doi.org/10.3897/neobiota.94.126714
