The goal of this project is to develop, and to apply to data, new theory for analysis of lifetime reproductive output (LRO). LRO plays an important role in ecology (the mean LRO determines whether a population grows or goes extinct) and evolution (the genetic variance in LRO determines the response to natural selection). We will develop new theory that will permit calculation of the mean, variance, and other statistics of LRO from ecological demographic data (a matrix population model or equivalent). Although mean values of LRO have often been calculated, variances and higher order moments are either unknown or have received little attention. Our research will, for the first time, determine LRO variance in both discrete- and continuous-time models of both linear and density-dependent populations. Our methods use absorbing Markov chains with stochastic rewards. We will develop models for reproduction that incorporate both stochastic variation and inter-individual heterogeneity. We will use these analyses to partition observed data on variance in lifetime reproductive output into stochastic and heterogeneous components. We will apply the theory to two new ecological databases containing demographic data for plants (over 900 species) and animals (over 1300 species), with accompanying taxonomic, geographical, habitat, and experimental metadata. We will use these demographic data to determine the relation of the mean, variance, and other LRO statistics to life history traits, environmental stressors, and phylogenetic relations. Our results will be applicable to any species with any kind of life cycle, with eventual implications for agricultural pest control and human populations.

We developed a method for collecting synthetic microfibers released during washing of clothes, which can be employed by citizen scientists. Our method uses a combination of commercial microfiber-blocking washing bags and simple lint rollers. In a small pilot study, 8 citizen scientists tested the method while washing their normal clothes. Together with data collected on the citizens’ washing habits, we obtained first insights into the amounts of microfibers that were released. The approach developed in CIFINDER lays the foundation for a follow-up project in which 100 citizen scientists are currently collecting microfibers during 10 washes in their own homes.

Our planet is changing rapidly. To understand and forecast how ecosystems are affected by global change, ecology should become a predictive science. We will build a unique virtual research environment that will facilitate this transformation, capitalizing on recent advances in Big Data science. This will enable ecologists to link scattered long-term data on plants, animals, and the environment; share methods for data analysis, modelling, and simulation; and build digital replicas of entire ecosystems (“Digital Twins”). This will transform our ability to understand and predict how ecosystems will respond under different scenarios and mitigation measures, fostering scientific breakthroughs and societal impact.

Public health monitoring is a crucial part of all governmental health programs that aim to capture both the physical and mental health of communities. Building upon the need to develop evidence-based public health models and the One Health initiative, our proposal “SewHealth - Advancing wastewater-based epidemiology towards a comprehensive community-wide health indicator” addresses these challenges.

Animal movement has important implications for survival, reproduction, ecosystem functioning, and human-wildlife interactions. Tracking changes in movement is possible with advanced bio-logging devices and remote sensing techniques such as radar but requires a computationally intensive research approach. Data must be transferred and stored in systems easily accessible to multiple researchers and tools are needed for fast paced data exploration and integration. The aim of this project is to develop scalable tools for rapid and collaborative exploration, annotation and storage of animal movement data.