Els Weinans

Digital technology plays an increasingly important role in wildlife management and conservation, by enhancing monitoring capabilities and reshaping human-wildlife interactions. However, the transformative potential of these digital solutions for coexistence remains unclear. This paper presents a novel framework to assess the transformative potential of digital systems in wildlife management and conservation, focusing on two key factors: Digital Maturity, which evaluates technical sophistication of digital systems, and Systemic Depth, which measures their capacity for enabling lasting change. We used this framework to evaluate 524 studies in a systematic literature review in wildlife management and conservation, and found that although sometimes higher Digital Maturity or Systemic Depth was achieved, overall the transformative potential of applied digital systems was still low. Studies that scored high emphasize interdisciplinary collaboration, adaptability, data sharing, and technologies such as machine learning. This research highlights achieving transformative potential requires a holistic approach integrating ecological, social, and technological perspectives.

There is a growing interest to analyze physiological data from a complex systems perspective. Accelerometer data is one type of data that is easy to obtain but often difficult to analyze for insights beyond basic levels of description. Previous work hypothesizes that an individual’s activity pattern can be seen as a complex dynamical system. Here, we explore this hypothesis further by investigating whether complexity-based measures quantifying repetitiveness and fragmentation of activity captured via accelerometer can detect health differences beyond traditional measures. Our results demonstrate that healthy individuals have a higher regularity (indicated by a lower correlation dimension), a higher probability of activity after a period of rest, and a lower probability of a period of rest after a period of activity compared with patients living with Myotonic Dystrophy type I (DM1), a chronic, progressive, complex, multisystem disease. For the correlation dimension, this difference was independent of the average, coefficient of variation and autocorrelation of the activity signals. This suggests that the correlation dimension can extract clinically relevant information from accelerometer data. Therefore, our results corroborate the idea that a complexity perspective may help to reveal the emergent characteristics of health and disease.

Tipping points characterize the situation when a system experiences abrupt, rapid, and sometimes irreversible changes in response to only a gradual change in environmental conditions. Given that such events are in most cases undesirable, numerous approaches have been proposed to identify if a system is approaching a tipping point. Such approaches have been termed early warning signals and represent a set of methods for identifying statistical changes in the underlying behaviour of a system across time or space that would be indicative of an approaching tipping point. Although the idea of early warnings for a class of tipping points is not new, in the last 2 decades, the topic has generated an enormous amount of interest, mainly theoretical. At the same time, the unprecedented amount of data originating from remote sensing systems, field measurements, surveys, and simulated data, coupled with innovative models and cutting-edge computing, has made possible the development of a multitude of tools and approaches for detecting tipping points in a variety of scientific fields. However, we miss a complete picture of where, how, and which early warnings have been used so far in real-world case studies. Here we review the literature of the last 20 years to show how the use of these indicators has spread from ecology and climate to many other disciplines. We document what metrics have been used; their success; and the field, system, and tipping points involved. We find that, despite acknowledged limitations and challenges, in the majority of the case studies we reviewed, the performance of most early warnings was positive in detecting tipping points. Overall, the generality of the approaches employed – the fact that most early warnings can in theory be observed in many dynamical systems – explains the continuous multitude and diversification in their application across scientific domains.

Our ability to deal with external changes is determined by our collective willingness to transform and adopt new technologies. These factors are driven by people's opinion on the change itself and the proposed policies. Humans constantly update their opinion by integrating new information they hear with their values, which helps them make a judgement about that new information. Here, we create an agent-based model that explicitly incorporates the concept of values to explore possible drivers of opinion dynamics. In the model, we explore several factors and perform local and global sensitivity analysis to test their individual and interaction effects. We find that consensus formation in the model is mainly determined by factors related to (1) the amount of stochasticity in the opinion updating procedure and (2) the relative ease with which old links are removed and new links are created. Our results demonstrate how opinions and values may co-evolve. Furthermore, they may help in understanding human responses to new policies such as covid-related restrictions or calls to shift to a more plant-based diet.

Tipping points characterize the situation when a system experiences abrupt, rapid and sometimes irreversible changes. Given that such changes are in most cases undesirable, numerous approaches have been proposed to identify if a system is close to a tipping point. Such approaches have been termed early-warning signals and represent a set of methods for identifying statistical changes in the underlying behavior of a system across time or space that would be indicative of an approaching tipping point. Although the idea of early-warnings for a class of tipping points is not new, in the last two decades, the topic generated an enormous amount of interest, mainly theoretical. At the same time, the unprecedented amount of data originating from remote sensing systems, field measurements, surveys and simulated data, coupled with innovative models and cutting-edge computing, has made possible the development of a multitude of tools and approaches for detecting tipping points in a variety of scientific fields. Yet, we miss a complete picture of where, how, and which early-warnings have been used so far in real-world case studies. Here we review the literature of the last 20 years to show how the use of these indicators has spread from ecology and climate to many other disciplines. We document what metrics have been used, their success as well as the field, system and tipping point involved. We find that, despite acknowledged limitations and challenges, in the majority of the case-studies we reviewed the performance of most early-warnings was positive in detecting tipping points. Overall, the generality of the approaches employed – the fact that most early-warnings can in theory be observed on any dynamical system – explains the continuous multitude and diversification in their application across scientific domains.

Tipping points characterize the situation when a system experiences abrupt, rapid and sometimes irreversible changes. Given that such changes are in most cases undesirable, numerous approaches have been proposed to identify if a system is close to a tipping point. Such approaches have been termed early-warning signals and represent a set of methods for identifying statistical changes in the underlying behavior of a system across time or space that would be indicative of an approaching tipping point. Although the idea of early-warnings for a class of tipping points is not new, in the last two decades, the topic generated an enormous amount of interest, mainly theoretical. At the same time, the unprecedented amount of data originating from remote sensing systems, field measurements, surveys and simulated data, coupled with innovative models and cutting-edge computing, has made possible the development of a multitude of tools and approaches for detecting tipping points in a variety of scientific fields. Yet, we miss a complete picture of where, how, and which early-warnings have been used so far in real-world case studies. Here we review the literature of the last 20 years to show how the use of these indicators has spread from ecology and climate to many other disciplines. We document what metrics have been used, their success as well as the field, system and tipping point involved. We find that, despite acknowledged limitations and challenges, in the majority of the case-studies we reviewed the performance of most early-warnings was positive in detecting tipping points. Overall, the generality of the approaches employed – the fact that most early-warnings can in theory be observed on any dynamical system – explains the continuous multitude and diversification in their application across scientific domains.

Tipping points characterize the situation when a system experiences abrupt, rapid and sometimes irreversible changes. Given that such changes are in most cases undesirable, numerous approaches have been proposed to identify if a system is close to a tipping point. Such approaches have been termed early-warning signals and represent a set of methods for identifying statistical changes in the underlying behavior of a system across time or space that would be indicative of an approaching tipping point. Although the idea of early-warnings for a class of tipping points is not new, in the last two decades, the topic generated an enormous amount of interest, mainly theoretical. At the same time, the unprecedented amount of data originating from remote sensing systems, field measurements, surveys and simulated data, coupled with innovative models and cutting-edge computing, has made possible the development of a multitude of tools and approaches for detecting tipping points in a variety of scientific fields. Yet, we miss a complete picture of where, how, and which early-warnings have been used so far in real-world case studies. Here we review the literature of the last 20 years to show how the use of these indicators has spread from ecology and climate to many other disciplines. We document what metrics have been used, their success as well as the field, system and tipping point involved. We find that, despite acknowledged limitations and challenges, in the majority of the case-studies we reviewed the performance of most early-warnings was positive in detecting tipping points. Overall, the generality of the approaches employed – the fact that most early-warnings can in theory be observed on any dynamical system – explains the continuous multitude and diversification in their application across scientific domains.

Tipping points characterize the situation when a system experiences abrupt, rapid and sometimes irreversible changes. Given that such changes are in most cases undesirable, numerous approaches have been proposed to identify if a system is close to a tipping point. Such approaches have been termed early-warning signals and represent a set of methods for identifying statistical changes in the underlying behavior of a system across time or space that would be indicative of an approaching tipping point. Although the idea of early-warnings for a class of tipping points is not new, in the last two decades, the topic generated an enormous amount of interest, mainly theoretical. At the same time, the unprecedented amount of data originating from remote sensing systems, field measurements, surveys and simulated data, coupled with innovative models and cutting-edge computing, has made possible the development of a multitude of tools and approaches for detecting tipping points in a variety of scientific fields. Yet, we miss a complete picture of where, how, and which early-warnings have been used so far in real-world case studies. Here we review the literature of the last 20 years to show how the use of these indicators has spread from ecology and climate to many other disciplines. We document what metrics have been used, their success as well as the field, system and tipping point involved. We find that, despite acknowledged limitations and challenges, in the majority of the case-studies we reviewed the performance of most early-warnings was positive in detecting tipping points. Overall, the generality of the approaches employed – the fact that most early-warnings can in theory be observed on any dynamical system – explains the continuous multitude and diversification in their application across scientific domains.