Digitalisation of Natural Complexity to Solve Socially Relevant Ecological Problems
Mecklenburg-Vorpommern’s Excellence Programme "Digitisation in Research" has granted its support to the consortium "DIG-IT! Digitisation of Natural Complexity to Solve Socially Relevant Ecological Problems" that is being coordinated by Prof. Martin Wilmking. Between July 2019 and June 2022, researchers will have 2 million euros at their disposal to develop a methodological toolbox that can independently capture and categorise ecological image and audio data using machine learning techniques (deep convolutional neural networks). Partners are the Fraunhofer Institute for Computer Graphics Research Rostock (Prof. Uwe von Lukas), the Chair of Biomathematics (Prof. Mareike Fischer) and working groups at the Institute of Botany und Landscape Ecology (Profs. Joosten, Kreyling, Wilmking) and the Zoological Institute and Museum (Prof. Gerald Kerth).
By exploring the opportunities of digitisation for the ecological sciences, DIG-IT! will meet pressing ecological questions of high societal relevance with a future-oriented arsenal of methods, thereby qualifying digitally competent ecologists and ecologically experienced biomathematicians and computer scientists. Dig-It! will address a broad array of questions including (but not limited to) the performance features and stability of ecosystems subject to climatic and land-use change, species protection and innovative environmental monitoring. The overarching goal is to facilitate a "quantum leap" for the field of ecology through the development of universally applicable methods using self-learning algorithms ("Deep Convolutional Neural Networks"), because in the digital age the challenge no longer lies in the amount of available primary data, but in its evaluation. For this purpose, DIG-IT! will combine the developmental expertise for the automated analysis of image data (Fraunhofer Institute for Computer Graphics, Rostock, and Chair of Biomathematics at the University of Greifswald) with the application in urgent ecological questions (Botany / Landscape Ecology / Zoology University of Greifswald).
Extract from the jury vote:
"The DIG-IT! project aims to address urgent ecological issues through the use of digital technologies, with the evaluation of primary data posing a particular challenge. (...) The jury is convinced of the project’s scientific excellence. The idea of developing an ecological toolbox is particularly impressive. It also appreciates the approach of docking onto existing research excellence projects such as WETSCAPES and RESPONSE [DFG Research Training Group "Biological RESPONSEs to Novel and Changing Environments"] and primarily using their findings to promote young scientists."
The Fraunhofer Institute’s subproject looks at the implementation of Deep Learning methods including its application for image data derived from ecological research. The strong structural form of artificial neural networks enables machine learning to identify target features and characteristics in images. Machine learning methods will significantly increase the efficiency of image feature identification through automated evaluation, enabling ecologists to process much larger digital image databases. Sample data for testing these methods will be supplied by the project’s research groups from the field of biology that include a broad spectrum of data sources from pollen and roots, to bats, ensuring universal applicability in ecological sciences. The analyses (that shall be supported by a doctoral candidate) will focus on the automated detection of previously defined objects in an image, for instance as required when counting pollen in a microscopic image. Furthermore, the investigations will incorporate the subsequent automated segmentation that separate detected objects from background data. The aim is to refine the object margins from the surrounding rectangles towards exact outlines. This would enable isolated objects to be passed on to human experts or downstream software tools. Research tasks in the subproject are focused on “Deep Convolutional Neural Networks” given their proven suitability for evaluating image data. The method focus will shift gradually from “supervised” to “semi-supervised” until it reaches “unsupervised” classification. At this final stage, the training will require little to no data to be previously classified by humans. Additionally, the inclusion of semantical information (e.g. topology of root systems) will be assessed to enhance image analysis. This approach aims to improve the efficiency of image classification and processing, developing new fields of ecological research that rely on universally practicable methods.
Mareike Fischer is managing the subproject “Biomathematics” with the help of a doctoral candidate and a PostDoc. Biomathematics mediates between mathematic theories and biological application, providing new tools to analyse biological data. M. Fischer will mediate between machine-learning theoreticians and ecologists by incorporating biomathematical modelling and statistical evaluation. The subproject “Roots” will focus on modelling root growth temporally by using graph theory and explore whether the level of root growth is statistically suitable for determining nutrient distribution in the soil. Additionally, multidimensional data collected on bats, wood and pollen are further study targets for promoting deep learning methods in ecological research. Baseline data required to train machine-learning approaches are numerous and include DNA (field of expertise for M. Fischer), sound recordings of bats, or the chemical properties of wood and pollen. DIG-IT!’s Deep Learning methods will identify data characteristics that are neither visible to humans nor can be derived from additional information.
Project coordination + wood anatomy
Martin Wilmking is project coordinator for DIG-IT! and manages the subproject “Wood Anatomy”. Some of the measures will be performed by a doctoral candidate in collaboration with partners from Fraunhofer IGD and associated master’s dissertations. The interdisciplinary PostDoc working as “Catalyst and Multiplier” will link all members within the consortium (catalyst) and provide expertise from DIT-IT! to external users (multiplier). Further measures will take place in collaboration with ongoing research projects (e.g. “WETSCAPES” project) and in the dendro-ecological laboratory “DendroGreif”. Two members of technical staff are assigned to the coordinator: a part-time position (50 %) for supporting coordination and a full-time position for assisting the users with data acquisition, digital image processing, and data management.
Jürgen Kreyling manages the subproject “Roots”, in which he coordinates the “automated digitisation of dynamics in root systems of plants”. The subproject will establish the automated acquisition of various root characteristics (e.g. length, diameter etc.) from minirhizotron scans in various settings, ranging from simplified laboratory conditions to complex field conditions. The project will be realised by a doctoral candidate in collaboration with partners from Fraunhofer IGD and associated master’s dissertations. J. Kreyling’s working group is equipped with a greenhouse and established research sites in a local beech forest and the nature reserve “Karrendorfer Wiesen”, enabling high-resolution rhizotron scans over a prolonged period of time. Further research collaborations with the “WETCAPES” project are expected.
Automated identification of species/individuals of selected animal species
Gerald Kerth manages the subproject “automated identification of species/individuals for selected animal species”. The project will be realised by a doctoral candidate in collaboration with partners from Fraunhofer IGD and associated master’s dissertations. The required field data will only be collected from the wild, in particular from 10 different wintering grounds of bats in Mecklenburg-Vorpommern. The established methods for the digital determination of species will be applied at research sites belonging to the two DBU (Deutsche Bundestiftung Umwelt) projects "FUN" and "Living underground".
Hans Joosten is managing the subproject “pollen identification” that will be realised by a doctoral candidate in collaboration with partners from Fraunhofer IGD, the Chair of Biomathematics (UG), and associated master’s dissertations. Laboratory analyses will be predominantly conducted in the laboratories belonging to the working group Peatland Studies and Palaeoecology where specialised equipment is available for everything from coring, sample collection and preparation to pollen analysis. High-resolution images will be provided by microscopes at the Department of Biology’s Imaging Center. The established transect of the working group J. Kreyling will provide samples from beech trees in blossom. Seasonally stratified sediments will be sampled in collaboration with the German Research Centre for Geosciences (GFZ) in Potsdam.