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Extensive research has demonstrated that urbanization strongly alters ecological processes, often perniciously. However, quantifying the magnitude of urban effects and determining how generalized they can be across systems depends on the ways in which urbanization is measured and modelled. We coupled a formal literature survey with a novel conceptual framework to document and synthesize the myriad of metrics used to quantify urbanization. The framework enables clear cataloguing of urban metrics by identifying (a) the urban component measured, (b) the method of measurement, (c) the metric's spatial scale and (d) the metric's temporal nature. Thus, the framework comprehensively captures the what, how, where and when of urban metrics. We documented striking variability in urban metrics with respect to which urban components were measured as well as how, where and when they were quantified. Overall, our survey revealed that they tended to be: (a) structurally focused, (b) methodologically simplistic, (c) spatially variable and (d) temporally static. Synthesis and applications. Many metrics are used to quantify urbanization or ‘urban‐ness'. The variation in urban metrics complicates the development of theory, comparisons of findings across studies, and the implementation of management and conservation actions. To pave a clear path forward for more efficient and policy‐relevant urban research, we systematically organized urban metrics using a simple, flexible and comprehensive framework. The framework clarifies what urbanization actually means in empirical practice and identifies several crucial areas for future research, including: (a) systematic assessments of urban metrics across multiple scales, (b) an increased and judicious use of more complex urban metrics aimed at evaluating both mechanistic and broad‐scale correlative ecological hypotheses, and (c) an increased emphasis on the socio‐economic aspects of urban effects. Many metrics are used to quantify urbanization or ‘urban‐ness'. The variation in urban metrics complicates the development of theory, comparisons of findings across studies, and the implementation of management and conservation actions. To pave a clear path forward for more efficient and policy‐relevant urban research, we systematically organized urban metrics using a simple, flexible and comprehensive framework. The framework clarifies what urbanization actually means in empirical practice and identifies several crucial areas for future research, including: (a) systematic assessments of urban metrics across multiple scales, (b) an increased and judicious use of more complex urban metrics aimed at evaluating both mechanistic and broad‐scale correlative ecological hypotheses, and (c) an increased emphasis on the socio‐economic aspects of urban effects.

The vertical mixing mechanism (VMM) is one of the most active air‐sea coupling process forced by the ocean. With a 2‐year global coupled 5‐km ICON simulation, we examine the strength, robustness, and scales of the coupling via VMM as approximated by the relation between downwind sea surface temperature (SST) gradients and windstress divergence. While the coupling via VMM is active on the ocean mesoscales around O (100 km), large coherent SST fronts can support such coupling to span over scales larger than 500 km. We find that VMM operates on a wider range of temporal scales than previously thought. In particular, VMM is active not only on monthly but also on daily timescales, and is even present on hourly timescales. The link of VMM to SST variability indicates that the underlying SST is what makes the VMM a multi‐scale phenomenon.

Anthropogenic global change compromises forest resilience, with profound impacts to ecosystem functions and services. This synthesis paper reflects on the current understanding of forest resilience and potential tipping points under environmental change and explores challenges to assessing responses using experiments, observations and models. Forests are changing over a wide range of spatio‐temporal scales, but it is often unclear whether these changes reduce resilience or represent a tipping point. Tipping points may arise from interactions across scales, as processes such as climate change, land‐use change, invasive species or deforestation gradually erode resilience and increase vulnerability to extreme events. Studies covering interactions across different spatio‐temporal scales are needed to further our understanding. Combinations of experiments, observations and process‐based models could improve our ability to project forest resilience and tipping points under global change. We discuss uncertainties in changing CO₂concentration and quantifying tree mortality as examples. Synthesis. As forests change at various scales, it is increasingly important to understand whether and how such changes lead to reduced resilience and potential tipping points. Understanding the mechanisms underlying forest resilience and tipping points would help in assessing risks to ecosystems and presents opportunities for ecosystem restoration and sustainable forest management.

Due to the importance of arbuscular mycorrhizal fungi (AMF) in ecosystem pro-ductivity, a key ecological question is how do their communities assemble? As plant spatial patterns constitute a mosaic of AM fungi habitats, we hypothesized that AM fungal community assembly is determined by plant community structure, both in space and time.We tested our hypothesis by sampling individuals of two host-plant species, Brachypodium pinnatum and Elytrigia repens, from experimental communities cultivated in mesocosms, and assessed their AM fungal root colonizers by mass sequencing. We related AM fungal community structure to the distribution of neighbouring plant species at different spatio-temporal scales.We demonstrated that AM fungal community assembly depends mostly on past plant spatial patterns at a small spatial scale (5 cm), indicating that plants growing at given locations leave a footprint on the AM fungi community. This spatial scale of response was also influenced by the host-plant species, probably by its clonal propagation.Synthesis. Overall, we highlighted that processes involved in Arbuscular mycor-rhizal (AM) fungal community assembly do not operate at the rough scale of the overall plant community mosaic but are instead locally determined, delineating the AM fungal ‘eye-view’ of the host-plant community.

Forests around the world are changing as a result of human activity. These changes have substantial impacts on the resilience of forests, possibly pushing them towards tipping points. The objective of this Special Feature is to present research that fosters the understanding of forest resilience and potential tipping points under global change. This editorial summarizes the key findings of the seven papers in this Special Feature and puts them in the wider context of resilience thinking. Synthesis. The contributions to this Special Feature show that resilience is a useful concept to understand ecosystem change but that we have to develop a better understanding of the mechanisms and feedback loops involved in forest resilience and potential tipping points. Finally, this Special Feature presents evidence about how resilience thinking is used to better understand and manage degraded forests.

Changes in land use, climate, and host community are leading to increased complexity in eco‐epidemiological relationships and the emergence of zoonoses. This study investigates the changes in the prevalence of several Ixodes ricinus‐transmitted pathogens in questing ticks over a 10‐year interval (2011–2013, 2020) in natural and agricultural habitats of the Autonomous Province of Trento (North‐eastern Alps), finding an average prevalence of infection of 27.1%. Analysis of 2652 ticks, investigating four infectious agents (Borrelia burgdorferi sensu lato, Anaplasma spp., Rickettsia spp., and Babesia spp.), revealed the circulation of 11 different zoonotic pathogens, with varying infection rates across different years and habitats. In 2020, we found a decrease in Anaplasma phagocytophilum, associated with agricultural habitats, and Rickettsia spp., found in all habitats. In the same year, Babesia spp. increased in both habitats, similar to Borrelia burgdorferi sensu stricto, which was related to natural habitats. Co‐infections were identified in 8% of positive‐tested ticks with different spatiotemporal associations, primarily in natural settings. Our results provide new evidence that the risk of infection with tick‐borne pathogens in the Alpine region varies over time and in different environments, broadening the current information on co‐infection rates and the circulation of zoonotic pathogens, previously not reported in this area. This study examines the prevalence of tick‐borne pathogens in Ixodes ricinus ticks in the north‐eastern Italian Alps. It found an average infection rate of 27.1%, with 11 zoonotic pathogens identified, showing varying infection rates across different years and habitats. Notably, co‐infections were present in 8% of positive ticks, highlighting the dynamic risk of tick‐borne diseases in this region.

Heterochrony, the change in the rate or timing of development between ancestors and their descendants, plays a major role in evolution. When heterochrony produces polymorphisms, it offers the possibility to test hypotheses that could explain its success across environments. Amphibians are particularly suitable to exploring these questions because they express complex life cycles (i.e. metamorphosis) that have been disrupted by heterochronic processes (paedomorphosis: retention of larval traits in adults). The large phenotypic variation across populations suggests that more complex processes than expected are operating, but they remain to be investigated through multivariate analyses over a large range of natural populations across time. In this study, we compared the likelihood of multiple potential environmental determinants of heterochrony. We gathered data on the proportion of paedomorphic and metamorphic palmate newts (Lissotriton helveticus) across more than 150 populations during 10 years and used an information‐theoretic approach to compare the support of multiple potential processes. Six environmental processes jointly explained the proportion of paedomorphs in populations: predation, water availability, dispersal limitation, aquatic breathing, terrestrial habitat suitability and antipredator refuges. Analyses of variation across space and time supported models based on the advantage of paedomorphosis in favourable aquatic habitats. Paedomorphs were favoured in deep ponds, in conditions favourable to aquatic breathing (high oxygen content), with lack of fish and surrounded by suitable terrestrial habitat. Metamorphs were favoured by banks allowing easy dispersal. These results indicate that heterochrony relies on complex processes involving multiple ecological variables and exemplifies why heterochronic patterns occur in contrasted environments. On the other hand, the fast selection of alternative morphs shows that metamorphosis and paedomorphosis developmental modes could be easily disrupted in natural populations.

Benefiting from recent advantages in location-aware technologies, movement data are becoming ubiquitous. Hence, numerous research topics with respect to movement data have been undertaken. Yet, the research of dynamic interactions in movement data is still in its infancy. In this paper, we propose a hybrid approach combining the multi-temporal scale spatio-temporal network (MTSSTN) and the continuous triangular model (CTM) for exploring dynamic interactions in movement data. The approach mainly includes four steps: first, the relative trajectory calculus (RTC) is used to derive three types of interaction patterns; second, for each interaction pattern, a corresponding MTSSTN is generated; third, for each MTSSTN, the interaction intensity measures and three centrality measures (i.e., degree, betweenness and closeness) are calculated; finally, the results are visualized at multiple temporal scales using the CTM and analyzed based on the generated CTM diagrams. Based on the proposed approach, three distinctive aims can be achieved for each interaction pattern at multiple temporal scales: (1) exploring the interaction intensities between any two individuals; (2) exploring the interaction intensities among multiple individuals, and (3) exploring the importance of each individual and identifying the most important individuals. The movement data obtained from a real football match are used as a case study to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach is useful in exploring dynamic interactions in football movement data and discovering insightful information.

Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products.

This paper proposes Feature fusion and Dilated causal convolution model for Bearing Remaining useful life Prediction (FDBRP), an integrated framework for accurate Remaining Useful Life (RUL) prediction of rolling bearings that combines three key innovations: (1) a data augmentation module employing sliding-window processing and two-dimensional feature concatenation with label normalization to enhance signal representation and improve model generalizability, (2) a feature fusion module incorporating an enhanced graph convolutional network for spatial modeling, an improved multi-scale temporal convolution for dynamic pattern extraction, and an efficient multi-scale attention mechanism to optimize spatiotemporal feature consistency, and (3) an optimized dilated convolution module utilizing interval sampling to expand the receptive field, and combines the residual connection structure to realize the regularization of the neural network and enhance the ability of the model to capture long-range dependencies. Experimental validation showcases the effectiveness of proposed approach, achieving a high average score of 0.756564 and demonstrating a lower average error of 10.903656 in RUL prediction for test bearings compared to state-of-the-art benchmarks. This highlights the superior RUL prediction capability of the proposed methodology.