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Extreme weather poses a major challenge to global food security by causing sharp drops in crop yield and supply. International crop trade can potentially alleviate such challenge by reallocating crop commodities. However, the influence of extreme weather stress and synchronous crop yield anomalies on trade linkages among countries remains unexplored. Here we use the international wheat trade network, develop two network-based covariates (i.e., difference in extreme weather stress and short-term synchrony of yield fluctuations between countries), and test specialized statistical and machine-learning methods. We find that countries with larger differences in extreme weather stress and synchronous yield variations tend to be trade partners and with higher trade volumes, even after controlling for factors conventionally implemented in international trade models (e.g., production level and trade agreement). These findings highlight the need to improve the current international trade network by considering the patterns of extreme weather stress and yield synchrony among countries.

Multilayer networks continue to gain significant attention in many areas of study, particularly due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socioenvironmental ecosystems. However, clustering of multilayer networks, especially using the information on higher-order interactions of the system entities, still remains in its infancy. In turn, higher-order connectivity is often the key in such multilayer network applications as developing optimal partitioning of critical infrastructures in order to isolate unhealthy system components under cyber-physical threats and simultaneous identification of multiple brain regions affected by trauma or mental illness. In this paper, we introduce the concepts of topological data analysis to studies of complex multilayer networks and propose a topological approach for network clustering. The key rationale is to group nodes based not on pairwise connectivity patterns or relationships between observations recorded at two individual nodes but based on how similar in shape their local neighborhoods are at various resolution scales. Since shapes of local node neighborhoods are quantified using a topological summary in terms of persistence diagrams, we refer to the approach as clustering using persistence diagrams (CPD). CPD systematically accounts for the important heterogeneous higher-order properties of node interactions within and in-between network layers and integrates information from the node neighbors. We illustrate the utility of CPD by applying it to an emerging problem of societal importance: vulnerability zoning of residential properties to weather- and climate-induced risks in the context of house insurance claim dynamics.

Vertical movements can expose individuals to rapid changes in physical and trophic environments—for aquatic fauna, dive profiles from biotelemetry data can be used to quantify and categorize vertical movements. Inferences on classes of vertical movement profiles typically rely on subjective summaries of parameters or statistical clustering techniques that utilize Euclidean matching of vertical movement profiles with vertical observation points. These approaches are prone to subjectivity, error, and bias. We used machine learning approaches on a large dataset of vertical time series (N = 28,217 dives) for 31 post‐nesting leatherback turtles (Dermochelys coriacea). We applied dynamic time warp (DTW) clustering to group vertical movement (dive) time series by their metrics (depth and duration) into an optimal number of clusters. We then identified environmental covariates associated with each cluster using a generalized additive mixed‐effects model (GAMM). A convolutional neural network (CNN) model, trained on standard dive shape types from the literature, was used to classify dives within each DTW cluster by their shape. Two clusters were identified with the DTW approach—these varied in their spatial and temporal distributions, with dependence on environmental covariates, sea surface temperature, bathymetry, sea surface height anomaly, and time‐lagged surface chlorophyllaconcentrations. CNN classification accuracy of the five standard dive profiles was 95%. Subsequent analyses revealed that the two clusters differed in their composition of standard dive shapes, with each cluster dominated by shapes indicative of distinct behaviors (pelagic foraging and exploration, respectively). The use of these two machine learning approaches allowed for discrete behaviors to be identified from vertical time series data, first by clustering vertical movements by their movement metrics (DTW) and second by classifying dive profiles within each cluster by their shapes (CNN). Statistical inference for the identified clusters found distinct relationships with environmental covariates, supporting hypotheses of vertical niche switching and vertically structured foraging behavior. This approach could be similarly applied to the time series of other animals utilizing the vertical dimension in their movements, including aerial, arboreal, and other aquatic species, to efficiently identify different movement behaviors and inform habitat models.