Early Warning Signals Research Articles

Crack coalescence prediction and load-bearing mechanism of defective specimen based on computer vision recognition model

The coalescence of cracks in rock, triggered by external stress or geological activities, plays a pivotal role in determining the mechanical properties and stability of rock structures. Consequently, the prediction method of crack coalescence become critical tools in ensuring the safety and stability of geotechnical engineering facilities. In this paper, based on the strain field data obtained by digital image correlation (DIC), a set of programs was developed to automatically identify the values and percentage changes of different strain intervals in the strain field of the specimen. Then, a computer vision recognition (CVR) model is established to study the process and prediction of crack coalescence in sandstone specimens with open flaws. This method overcomes the limitations, subjectivity and unpredictability of the traditional method of identifying cracks through artificial vision. The results show that the increase of the flaw width causes the displacement trend to be squeezed and deflected along the width direction, thereby changing the angle of crack initiation and exhibiting different forms of crack coalescence. The increase in the inclination angle of the flaw leads to an increase in the effective compressive area (ECA) of sandstone, and the magnitude of ECA is positively correlated with the uniaxial compressive strength (UCS) of the sandstone. Additionally, the CVR model identifies two types of numerical fluctuation signals before crack coalescence, namely the plastic characteristic signal (PCS) in the early stage of the experiment and the early-warning signal (EWS) near the period of crack coalescence, where EWS can be used as a predictive signal for crack coalescence. The research results provide a new algorithm technical support for the process analysis and prediction of crack coalescence, and provide a basis for early warning of rock mass engineering disasters.

An early warning indicator trained on stochastic disease-spreading models with different noises.

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modelling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreaks by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

Machine-learning nowcasting of the Atlantic Meridional Overturning Circulation

The Atlantic Meridional Overturning Circulation (AMOC) is a significant component of the global ocean system, which has so far ensured a relatively warm climate for the North Atlantic and mild conditions in regions, such as Western Europe. The AMOC is also critical for the global climate. The complexity of the dynamical system underlying the AMOC is so vast that a long-term assessment of the potential risk of AMOC collapse is extremely challenging. However, short-term prediction can lead to accurate estimates of the dynamical state of the AMOC and possibly to early warning signals for guiding policy making and control strategies toward preventing AMOC collapse in the long term. We develop a model-free, machine-learning framework to predict the AMOC dynamical state in the short term by employing five datasets: MOVE and RAPID (observational), AMOC fingerprint (proxy records), and AMOC simulated fingerprint and CESM AMOC (synthetic). We demonstrate the power of our framework in predicting the variability of the AMOC within the maximum prediction horizon of 12 or 24 months. A number of issues affecting the prediction performance are investigated.

Tipping points in coupled human–environment system models: a review

Abstract. Mathematical models that couple human behavior to environmental processes can offer valuable insights into how human behavior affects various types of ecological, climate, and epidemiological systems. This review focuses on human drivers of tipping events in coupled human–environment systems where changes to the human system can abruptly lead to desirable or undesirable new human–environment states. We use snowball sampling from relevant search terms to review the modeling of social processes – such as social norms and rates of social change – that are shown to drive tipping events, finding that many affect the coupled system depending on the system type and initial conditions. For example, tipping points can manifest very differently in human extraction versus human emission systems. Some potential interventions, such as reducing costs associated with sustainable behavior, have intuitive results. However, their beneficial outcomes via less obvious tipping events are highlighted. Of the models reviewed, we found that greater structural complexity can be associated with increased potential for tipping events. We review generic and state-of-the-art techniques in early warning signals of tipping events and identify significant opportunities to utilize digital social data to look for such signals. We conclude with an outline of challenges and promising future directions specific to furthering our understanding and informing policy that promotes sustainability within coupled human–environment systems. Non-technical summary. Mathematical models that include interactions between humans and the environment can provide valuable information to further our understanding of tipping points. Many social processes such as social norms and rates of social change can affect these tipping points in ways that are often specific to the system being modeled. Higher complexity of social structure can increase the likelihood of these transitions. We discuss how data are used to predict tipping events across many coupled systems.

Observation-inferred resilience loss of the Amazon rainforest possibly due to internal climate variability

Abstract. Recent observation-based studies suggest that the Amazon rainforest has lost substantial resilience since 1990, indicating that the forest might undergo a critical transition in the near future due to global warming and deforestation. The idea is to use trends in a lag-1 auto-correlation of leaf density as an early-warning signal of an imminent critical threshold for rainforest dieback. Here we test whether the observed change in auto-correlations could arise from internal variability using historical and control simulations of nine sixth-generation Earth system model ensembles (Phase 6 of the Coupled Model Intercomparison Project, CMIP6). We quantify trends in the leaf area index auto-correlation from both models and satellite-observed vegetation optical depth from 1990 to 2017. Four models reproduce the observed trend with at least one historical realization whereby the observations lie at the upper limit of model variability. Three out of these four models exhibit similar behavior in control runs, suggesting that historical forcing is not necessary for simulating the observed trends. Furthermore, we do not observe a critical transition in any future runs under the strongest greenhouse gas emission scenario (SSP5-8.5) until 2100 in the four models that best reproduce the past observed trends. Hence, the currently observed trends could be caused simply by internal variability and, unless the data records are extended, have limited applicability as an early-warning signal. Our results suggest that the current rapid decline in the Amazon rainforest coverage is not foremost caused by global warming.

Early warning signals for predicting cryptomarket vendor success using dark net forum networks

In this work we focus on identifying key players in dark net cryptomarkets that facilitate online trade of illegal goods. Law enforcement aims to disrupt criminal activity conducted through these markets by targeting key players vital to the market’s existence and success. We particularly focus on detecting successful vendors responsible for the majority of illegal trade. Our methodology aims to uncover whether the task of key player identification should center around plainly measuring user and forum activity, or that it requires leveraging specific patterns of user communication. We focus on a large-scale dataset from the Evolution cryptomarket, which we model as an evolving communication network. Results indicate that user and forum activity, measured through topic engagement, is best able to identify successful vendors. Interestingly, considering users with higher betweenness centrality in the communication network further improves performance, also identifying successful vendors with moderate activity on the forum. But more importantly, analyzing the forum data over time, we find evidence that attaining a high betweenness score comes before vendor success. This suggests that the proposed network-driven approach of modelling user communication might prove useful as an early warning signal for key player identification.

Analysis of traditional Chinese medicine syndrome elements and clinical symptoms in prediabetes: A systematic review.

Prediabetes mellitus (PDM) and impaired glucose regulation precedes diabetes and serve as early warning signals. A 2018 Chinese epidemiological survey reported prediabetes at 25.5% prevalence and type 2 diabetes at 10.8%, respectively. Untreated carries one-third of the risk of diabetes progression. This study aimed to understand traditional Chinese medicine syndromes in PDM to guide clinical practice and diabetes prevention. We systematically searched the Chinese and English literature in PubMed, EMBASE, Sinomed, CNKI, VIP, Wanfang until March 31, 2023. We manually explored the Chinese prediabetes literature, trial registrations, and references, adhering to predefined criteria. The results were independently summarized by 2 researchers. Statistical analysis was performed using EXCEL, IBM SPSS 27.0, and IBM SPSS Modeler 18.0, with data mining techniques including association and cluster analysis. Analysis of 23 clinical trials (8943 patients) identified phlegm dampness syndrome as predominant, with qi deficiency, dampness, and phlegm as the principal pathogenic elements. Spleen syndrome elements dominated, with a priori correlation analysis favoring spleen dampness. The prevalent PDM clinical symptoms include amnesia, mental fatigue, limb fatigue, dizziness, and lumbar discomfort. Prediabetes is strongly associated with spleen dampness, highlighting its role. Common traditional Chinese medicine syndrome elements include qi deficiency, phlegm, and dampness. Clinical diagnosis and treatment should prioritize syndrome differentiation and emphasize spleen-focused approaches. Although limited research exists on prediabetes syndromes, further exploration of PDM and spleen dampness is crucial.

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that about one third of diabetes patients have signs of diabetic retinopathy, and another third may suffer from serious retinopathy that threatens vision. However, the pathogenesis of diabetic retinopathy is still unclear, and there is no systematic method to detect the onset of the disease and effectively predict its occurrence. In this study, we used medical detection data from diabetic retinopathy patients to determine key biomarkers that induce disease onset through back propagation neural network algorithm and hierarchical clustering analysis, ultimately obtaining early warning signals of the disease. The key markers that induce diabetic retinopathy have been detected, which can also be used to explore the induction mechanism of disease occurrence and deliver strong warning signal before disease occurrence. We found that multiple clinical indicators that form key markers, such as glycated hemoglobin, serum uric acid, alanine aminotransferase are closely related to the occurrence of the disease. They respectively induced disease from the aspects of the individual lipid metabolism, cell oxidation reduction, bone metabolism and bone resorption and cell function of blood coagulation. The key markers that induce diabetic retinopathy complications do not act independently, but form a complete module to coordinate and work together before the onset of the disease, and transmit a strong warning signal. The key markers detected by this algorithm are more sensitive and effective in the early warning of disease. Hence, a new method related to key markers is proposed for the study of diabetic microvascular lesions. In clinical prediction and diagnosis, doctors can use key markers to give early warning of individual diseases and make early intervention.

Ultrasound propagation characteristics within the bone tissue of miniature ultrasound probes: implications for the spinal navigation of pedicle screw placement.

The accuracy of pedicle screw fixation is crucial for patient safety. Traditional navigation methods based on computed tomography (CT) imaging have several limitations. Therefore, this study aimed to investigate the ultrasonic propagation characteristics of bone tissue and their relationship with CT imaging results, as well as the potential application of ultrasound navigation in pedicle screw fixation. The study used three bovine spine specimens (BSSs) and five human vertebral allograft bones (HABs) to progressively decrease the thickness of the cancellous bone layer, simulating the process of pedicle screw perforation. Five unfocused miniature ultrasound probes with frequencies of 2.2, 2.5, 3, 12, and 30 MHz were employed for investigating the ultrasonic propagation characteristics of cancellous and cortical bone through ultrasound transmission and backscatter experiments. The CT features of the bone tissue was obtained with the Skyscan 1174 micro-CT scanner (Bruker, Billerica, MA, USA). The experimental results demonstrated that low-frequency (2-3 MHz) ultrasound effectively penetrated the cancellous bone layer up to a depth of approximately 5 mm, with an attenuation coefficient below 10 dB/cm. Conversely, high-frequency (12 MHz) ultrasound exhibited significant signal attenuation in cancellous bone, reaching up to 55.8 dB/cm. The amplitude of the backscattered signal at the cancellous bone interface exhibited a negative correlation with the bone sample thickness (average r=-0.84), meaning that as the thickness of the cancellous bone layer on the cortical bone decreases, the backscattered signal amplitude gradually increases (P<0.05). Upon reaching the cortical bone interface, there was a rapid surge in echo signal amplitude, up to 8 times higher. Meanwhile, the statistical results indicated a significant correlation between the amplitude of the echo signal and the micro-CT scanning results of bone trabecular structure. Theoretically, using multiple ultrasonic probes (≥3) and regions of interest (ROIs) (≥5) has the potential to provide surgeons with early warning signals for pedicle perforation based on three or more successive increases in echo signal amplitude or a sudden substantial increase. The statistical results indicate a significant correlation between the amplitude of the echo signal and the micro-CT scanning results of bone trabeculae, suggesting the potential use of ultrasound as opposed to CT for real-time intraoperative bone navigation.

A MIRROR ANALYSIS OF ANTI-SEMITISM VERSUS RADICALIZATION: CONCEPTUAL ELEMENTS AND GENERATED RISKS

The outbreak of the Gaza conflict and the Israel-Hamas war demonstrated that a regional crisis can psychologically and socially impact society at an international level, noting the increase in anti-Semitic perceptions as well as the anticipation of a new trend of radicalization inspired by the Hamas ideology, directed against Israeli or Jewish objectives. These phenomena, as a result of technological dynamics and the digital role, can impact and distort the perception of individuals regardless of the region they belong to, generating high risks regarding the preservation of democratic values and principles and social order. In this sense, this article aims to extract the conceptual elements characteristic of the two phenomena in order to develop a comparative analysis that reflects in the mirror similarities and differences regarding their manifestation. Thus, the purpose of the paper is to raise society's awareness of the risks generated by the typology of these manifestations as well as facilitate the identification of early warning signals in relation to the upward trend generated at the international level.

ASHA-led community-managed intervention programme to improve maternal and neonatal outcomes in pregnant women with pregnancy hypertension: A study protocol.

The objective is to use the available technology in conjunction with the peripheral health workers to identify the early warning signs of pregnancy-induced hypertension (PIH), provide appropriate referral services and closely monitor the patient thereafter with the aim of improving the prognosis of the condition. Quasi-experimental design. The research will be carried out in Udupi District Karnataka, India. The study will involve 250 pregnant women in total as well as 50 ASHA workers. All the participants will undergo a PIH knowledge assessment, which will be followed by a PIH risk assessment for expectant mothers. There will be an implementation of the community-managed intervention programme (CMIP). The creation of a mobile application, ongoing prenatal follow-up and training for ASHAs on mobile app usage are all part of the CMIP. The results for mothers and newborns will be monitored and to learn more about the efficacy of CMIP and the difficulties ASHA workers and expectant mothers encounter in identifying the early warning signals of PIH a focused group discussion will be held. A number of research on pregnancy-induced hypertension have demonstrated the necessity for community-based treatments to be developed in order to improve the outcomes for expectant mothers and newborns. These programmes assist in managing hypertension throughout pregnancy. Conversely, ASHA employees have had a favourable influence on community-level health outcome management. Currently, technology is being used extensively in healthcare and is receiving favourable reception. Thus, in an effort to improve maternal and newborn outcomes, this study attempts to incorporate mobile technologies in addition to ASHA workers and pregnant women. If the CMIP proves to be beneficial, it might be implemented nationwide, particularly in isolated regions, to monitor expectant mothers and avoid PIH-related concerns.

CPMI: comprehensive neighborhood-based perturbed mutual information for identifying critical states of complex biological processes

BackgroundThere exists a critical transition or tipping point during the complex biological process. Such critical transition is usually accompanied by the catastrophic consequences. Therefore, hunting for the tipping point or critical state is of significant importance to prevent or delay the occurrence of catastrophic consequences. However, predicting critical state based on the high-dimensional small sample data is a difficult problem, especially for single-cell expression data.ResultsIn this study, we propose the comprehensive neighbourhood-based perturbed mutual information (CPMI) method to detect the critical states of complex biological processes. The CPMI method takes into account the relationship between genes and neighbours, so as to reduce the noise and enhance the robustness. This method is applied to a simulated dataset and six real datasets, including an influenza dataset, two single-cell expression datasets and three bulk datasets. The method can not only successfully detect the tipping points, but also identify their dynamic network biomarkers (DNBs). In addition, the discovery of transcription factors (TFs) which can regulate DNB genes and nondifferential ‘dark genes’ validates the effectiveness of our method. The numerical simulation verifies that the CPMI method is robust under different noise strengths and is superior to the existing methods on identifying the critical states.ConclusionsIn conclusion, we propose a robust computational method, i.e., CPMI, which is applicable in both the bulk and single cell datasets. The CPMI method holds great potential in providing the early warning signals for complex biological processes and enabling early disease diagnosis.

Innovative graph-based video processing methodology for collapse early warning of historic masonry buildings

In the present study an innovative video processing methodology is proposed for application to vibration data of historic masonry structures. The proposed methodology is based on graph theory and topology analysis applied to magnified videos in search of effective parameters for early-warning signals before the collapse of structures in case of earthquakes. The proposed method was validated through seismic tests of a brick-masonry mockup representing a vault of the mosque in the Palace of the Dey, Algiers. In particular, the seismic tests were carried out at increasing earthquake intensity up to the final collapse of the mockup. After processing the videos of the seismic tests by motion magnification method, the magnified video frames were transformed into a graph of the structure. Finally, several graph indices were calculated and monitored during the vibration. The monitored parameters were analyzed in search of potential threshold values suitable to generate an early warning signal. In particular, the inverse algebraic connectivity provided an early warning signal in the order of a few seconds before collapse. This was validated by comparison with an analogous signal provided at similar time by an accurate displacement lab measurements system based on optical markers positioned at several points of the tested mock-up.

Human Impacts Dominate Global Loss of Lake Ecosystem Resilience

AbstractStrengthening aquatic resilience to prevent adverse shifts is critical for preserving global freshwater biodiversity and advancing Sustainable Development Goals. Nonetheless, understanding the long‐term trends and underlying causes of lake ecosystem resilience at a global scale remains elusive. Here, we employ an innovative framework, integrating satellite‐derived water quality indices with early warning signals and machine learning techniques, to investigate the dynamics of resilience in 1,049 lakes worldwide during 2000–2018. Our results indicate that 46.7% of lakes are experiencing a significant decline in resilience, particularly since the early 2010s, closely associated with higher human population density and anthropogenic eutrophication. In contrast, most lakes situated in alpine regions exhibit an increase in resilience, probably benefiting from climate warming and wetting. Together, this study provides a novel way to monitor lake resilience and predict undesired transitions, and reveals a widespread erosion in the ability of lakes to withstand stressors associated with global change.

Microtopographic Variation as a Potential Early Indicator of Ecosystem State Change and Vulnerability in Salt Marshes

As global climate change alters the magnitude and rates of environmental stressors, predicting the extent of ecosystem degradation driven by these rapidly changing conditions becomes increasingly urgent. At the landscape scale, disturbances and stressors can increase spatial variability and heterogeneity — indicators that can serve as potential early warnings of declining ecosystem resilience. Increased spatial variability in salt marshes at the landscape scale has been used to quantify the propagation of ponding in salt marsh interiors, but ponding at the landscape scale follows a state change rather than predicts it. Here, we suggest a novel application of commonly collected surface elevation table (SET) data and explore millimeter-scale marsh surface microtopography as a potential early indicator of ecosystem transition. We find an increase in spatial variability using multiple metrics of microtopographic heterogeneity in vulnerable salt marsh communities across the North American Atlantic seaboard. Increasing microtopographic heterogeneity in vulnerable salt marshes mirrored increasing trends in variance when a tipping point is approached in other alternative stable state systems — indicating that early warning signals of marsh drowning and ecosystem transition are observable at small-spatial scales prior to runaway ecosystem degradation. Congruence between traditional and novel metrics of marsh vulnerability suggests that microtopographic metrics can be used to identify hidden vulnerability before widespread marsh degradation. This novel analysis can be easily applied to existing SET records expanding the traditional focus on vertical change to additionally encapsulate lateral processes.

Characterising early warning signals for critical transitions using a distribution moment approximation for the Fokker–Planck equation

Critical transitions are abrupt transition in time-varying systems. They occur in many different scientific contexts, ranging from climate and ecology to physiology and electricity transmission, and can be driven by different underlying mathematical structures. Here we focus on those driven by underlying bifurcations (so-called B-tipping), which are associated with qualitative changes in the underlying system energy landscape. Building on Kuehn’s fast-slow framework (Kuehn, 2011), we relax the assumptions of stationarity and reflecting boundary conditions by employing a distribution moment approach, which allows us to classify the utility of early warning signals (such as the variance) even when the timescales are not well separated, or the reflection boundary assumption is restrictive. This allows a more complete characterisation of moment-based early warning signals.

Optimal sampling of spatial patterns improves deep learning-based early warning signals of critical transitions.

Complex spatio-temporal systems like lakes, forests and climate systems exhibit alternative stable states. In such systems, as the threshold value of the driver is crossed, the system may experience a sudden (discontinuous) transition or smooth (continuous) transition to an undesired steady state. Theories predict that changes in the structure of the underlying spatial patterns precede such transitions. While there has been a large body of research on identifying early warning signals of critical transitions, the problem of forecasting the type of transitions (sudden versus smooth) remains an open challenge. We address this gap by developing an advanced machine learning (ML) toolkit that serves as an early warning indicator of spatio-temporal critical transitions, Spatial Early Warning Signal Network (S-EWSNet). ML models typically resemble a black box and do not allow envisioning what the model learns in discerning the labels. Here, instead of naively relying upon the deep learning model, we let the deep neural network learn the latent features characteristic of transitions via an optimal sampling strategy (OSS) of spatial patterns. The S-EWSNet is trained on data from a stochastic cellular automata model deploying the OSS, providing an early warning indicator of transitions while detecting its type in simulated and empirical samples.

Identification of fraudulent financial statements through a multi‐label classification approach

SummaryAlthough the financial audit controls in companies have advanced over the years, the number of corporate fraud instances is growing, thus raising the need for investigating the factors that can be used as early warning signals and developing effective systems for identifying financial fraud. In this paper, financial statements from 133 Greek companies listed in the Athens Stock Exchange over the period 2014 to 2019 are investigated, based on the fraud diamond theory. Financial data and corporate governance variables are used as inputs to data mining techniques to develop models that can identify patterns of irregularities in a company's financial reports. To this end, popular machine learning classification algorithms are employed in a novel multi‐label classification setting that not only identifies fraudulent cases but also considers the nature of the auditors' comments. The results indicate that the proposed multi‐label approach provides enhanced results compared to binary classification algorithms, avoiding inconsistent outputs with respect to the existence of different forms of manipulation of financial statements.

Identifying drivers of non-stationary climate-growth relationships of European beech

The future performance of the widely abundant European beech (Fagus sylvatica L.) across its ecological amplitude is uncertain. Although beech is considered drought-sensitive and thus negatively affected by drought events, scientific evidence indicating increasing drought vulnerability under climate change on a cross-regional scale remains elusive. While evaluating changes in climate sensitivity of secondary growth offers a promising avenue, studies from productive, closed-canopy forests suffer from knowledge gaps, especially regarding the natural variability of climate sensitivity and how it relates to radial growth as an indicator of tree vitality. Since beech is sensitive to drought, we in this study use a drought index as a climate variable to account for the combined effects of temperature and water availability and explore how the drought sensitivity of secondary growth varies temporally in dependence on growth variability, growth trends, and climatic water availability across the species' ecological amplitude.Our results show that drought sensitivity is highly variable and non-stationary, though consistently higher at dry sites compared to moist sites. Increasing drought sensitivity can largely be explained by increasing climatic aridity, especially as it is exacerbated by climate change and trees' rank progression within forest communities, as (co-)dominant trees are more sensitive to extra-canopy climatic conditions than trees embedded in understories. However, during the driest periods of the 20th century, growth showed clear signs of being decoupled from climate. This may indicate fundamental changes in system behavior and be early-warning signals of decreasing drought tolerance. The multiple significant interaction terms in our model elucidate the complexity of European beech's drought sensitivity, which needs to be taken into consideration when assessing this species' response to climate change.

Brood indicators are an early warning signal of honey bee colony loss-a simulation-based study.

Honey bees (Apis mellifera) are exposed to multiple stressors such as pesticides, lack of forage, and diseases. It is therefore a long-standing aim to develop robust and meaningful indicators of bee vitality to assist beekeepers While established indicators often focus on expected colony winter mortality based on adult bee abundance and honey reserves at the beginning of the winter, it would be useful to have indicators that allow detection of stress effects earlier in the year to allow for adaptive management. We used the established honey bee simulation model BEEHAVE to explore the potential of different indicators such as population size, number of capped brood cells, flight activity, abundance of Varroa mites, honey stores and a brood-bee ratio. We implemented two types of stressors in our simulations: 1) parasite pressure, i.e. sub-optimal Varroa treatment by the beekeeper (hereafter referred as Biotic stress) and 2) temporal forage gaps in spring and autumn (hereafter referred as Environmental stress). Neither stressor type could be detected by bee abundance or honey reserves at the end of the first year. However, all response variables used in this study did reveal early warning signals during the course of the year. The most reliable and useful measures seem to be related to brood and the abundance of Varroa mites at the end of the year. However, while in the model we have full access to time series of variables from stressed and unstressed colonies, knowledge of these variables in the field is challenging. We discuss how our findings can nevertheless be used to develop practical early warning indicators. As a next step in the interactive development of such indicators we suggest empirical studies on the importance of the number of capped brood cells at certain times of the year on bee population vitality.