Biology subjects

Kusch, E.

Publications and source records attributed to Kusch, E..

5 recordsLinked to original sources

bioRxiv2024

TwinEco: A Unified Framework for Dynamic Data-Driven Digital Twins in Ecology

1.A Digital Twin (DT) is a virtual replica of a physical object or process that is continuously updated at a certain frequency and can steer change on the physical system, enabling a seamless integration of observation, understanding, and action. Although initially applied primarily in industry, DT is emerging as a powerful tool in ecology, offering new possibilities for dynamic simulations of change in the biosphere. However, since DTs are relatively new in this field, there is currently no standard framework to guide their conceptualisation and development. Thus, DTs in ecological applications are already experiencing fragmentation in software concepts and design philosophies, leading to incompatibilities across DT implementations. This fragmentation risks undermining the progress and potential of the DT concept in ecology. A unifying framework, such as TwinEco, can address these discrepancies and establish a cohesive foundation for the effective adoption and integration of DTs across ecological domains. TwinEco is a modular framework designed to aid and harmonise ecologists efforts to build DTs. In doing so, TwinEco focuses on three major design goals: O_LIModularity, flexibility, and interoperability of DTs facilitated through distinct "components" nested within DT "layers". C_LIO_LIDynamic modeling of ecological processes and states that evolve over time. C_LIO_LILinking ecological modelling to downstream actions or decisions made on the ecological object or process of study. C_LI TwinEcos architecture builds upon the feedback loops and state management strategies introduced in the Dynamic Data-Driven Application Systems (DDDAS) paradigm, which has already inspired many DTs across scientific domains. We also discuss the usefulness and ease-of-use of TwinEco by demonstrating its applicability to computational case studies and suggesting future recommendations to the community of data infrastructure builders and modellers in regards to open considerations. By introducing a shared terminology and emphasising model-data fusion, TwinEco highlights the importance of a unified framework to avoid fragmentation in the burgeoning field of ecological digital twinning.

ecology↗

bioRxiv2023

A Novel Simulation Framework for Validation of Ecological Network Inference

O_LIUnderstanding how the differential magnitude and sign of ecological interactions vary across space is vital to assessing ecosystem resilience to biodiversity loss and predict community assemblies. This necessity for ecological network knowledge and their labour-intensive sampling requirements has spurred the creation of ecological network inference methodology. Recent research has identified inconsistencies in networks inferred using different approaches thus necessitating quantification of inference performance to facilitate choice of network inference approach. C_LIO_LIHere we develop a data simulation method to generate data products fit for network inference and subsequently quantify the validity of two well-established ecological interaction network inference methods - HMSC and COOCCUR. The simulation framework we present here can be parameterised using real-world information (e.g., biological interactions observed in-situ and bioclimatic niche preferences) thus representing network inference capabilities in real-world applications. Using this framework, it is thus possible to evaluate the performance of any ecological network inference approach. C_LIO_LIWe identify a concerningly large range in accuracy of inferred networks as compared to true, realisable association networks. These differences in inference accuracy are governed by a paradigm of input data types and environmental parameter estimation as previously suggested. To establish a workflow for quantification of network inference reliability, we suggest analysis procedures with which to explore inference and detection probabilities of association types of different identity and sign with respect to bioclimatic niche preferences and association strength of association partner-species. C_LIO_LIWith this study, we provide the groundwork with which to validate and compare ecological network inference methods, and ultimately vastly increase our ability to understand and predict species biodiversity across space and time. C_LI

bioinformatics↗

bioRxiv2023

Ecological network inference is not consistent across sales or approaches

Identifying the most suitable method of ecological network inference in line with individual research considerations is a non-trivial task, which significantly hinders adoption of network approaches to forest management applications. To advance the study of ecological networks and better guide their use in managing forest ecosystems, we propose a framework that aligns pairwise species-association inference methods with specific research questions, biological interaction types, data availability, and spatial scales of study. We motivate the adoption of this framework through an empirical comparison of multiple inference methods, highlighting substantial inconsistencies that arise across scales and methodologies. Using data on species distributions and attributes at local, regional, and continental scales for temperate conifer forests in North America, we show that network inference varies significantly depending on whether occurrence, abundance, or performance data are used and the degree to which confounding factors are accounted for. Across four widely used and/or cutting-edge inference methods (COOCCUR, NETASSOC, HMSC, NDD-RIM), we find notable disparities in both whole-network metrics and pairwise species associations, particularly at continental scales. These findings underscore that no single method is likely to universally outperforms others across scales, emphasizing the importance of choosing an inference approach that aligns with specific ecological and spatial contexts. Our framework aids in interpreting network topologies and interactions in light of these method- and datatype-driven variances, providing a structured approach to more reliably infer ecological associations and address complex network dynamics in forest management practices.

bioinformatics↗

bioRxiv2022

MOSAIC: A Unified Trait Database to Complement Structured Population Models

O_LIThe ecological sciences have joined the big data revolution. However, despite exponential growth in data availability, broader interoperability amongst datasets is still needed to unlock the potential of open access. The interface of demography and functional traits is well-positioned to benefit from said interoperability. Trait-based ecological approaches have been criticised because of their inability to predict fitness components, the core of demography; likewise, demographic approaches are data-hungry, and so using traits as ecological shortcuts to understanding and forecasting population viability could offer great value. C_LIO_LIHere, we introduce MOSAIC, an open-access trait database that unlocks the demographic potential stored in the COMADRE, COMPADRE, and PADRINO open-access databases. MOSAIC data have been digitised and curated through a combination of existing datasets and additional taxonomic and/or trait records sourced from primary literature. In its first release, MOSAIC (v. 1.0.0) includes 14 trait fields for 300 animal and plant species: biomass, height, growth determination, regeneration, sexual dimorphism, mating system, hermaphrodism, sequential hermaphrodism, dispersal capacity, type of dispersal, mode of dispersal, dispersal classes, volancy, and aquatic habitat dependency. MOSAIC also includes species-level phylogenies for 1,359 species and population-specific climate data where locations are recorded. C_LIO_LIUsing MOSAIC, we highlight a taxonomic mismatch of widely used trait databases with existing structured population models. Despite millions of trait records available in open-access databases, taxonomic overlap between open-access demographic and trait databases is <5%. We identify where traits of interest to ecologists can benefit from database integration and start to quantify traits that are poorly quantified (e.g., growth determination, modularity). C_LIO_LIThe MOSAIC database evidences the importance of improving interoperability in open-access efforts in ecology as well as the need for complementary digitisation to fill targeted taxonomic gaps. In addition, MOSAIC highlights emerging challenges associated with the disparity between locations where different trait records are sourced. C_LI

ecology↗

bioRxiv2021

Vegetation memory effects and their association with vegetation resilience in global drylands

AO_SCPLOWBSTRACTC_SCPLOWO_LIVegetation memory describes the effect of antecedent environmental and ecological conditions on the present ecosystem state and has been proposed as an important proxy for vegetation resilience. In particular, strong vegetation-memory effects have been identified in dryland regions, but the factors underlying the spatial patterns of vegetation memory remain unknown. C_LIO_LIWe aim to map the components and drivers of vegetation memory in dryland regions using state-of-the-art climate reanalysis data and refined approaches to identify vegetation-memory characteristics across dryland regions worldwide. C_LIO_LIUsing a framework which distinguishes between intrinsic and extrinsic ecological memory, we show that: (i) intrinsic memory is a much stronger component than extrinsic memory in the majority of dryland regions; and (ii) climate reanalysis data sets change the detection of extrinsic vegetation memory effects in some global dryland regions. C_LIO_LISynthesis. Our study offers a global picture of the vegetation response to two climate forcing variables using satellite data, information which is potentially relevant for mapping components and properties of vegetation responses worldwide. However, the large differences in the spatial patterns in intrinsic vegetation memory in our study compared to previous analyses show the overall sensitivity of this component in particular to the initial choice of extrinsic forcing variables. As a result, we caution against using the oversimplified link between intrinsic vegetation-memory and vegetation recovery rates at large spatial scales. C_LI

ecology↗