For microscale heterogeneous partial differential equations (PDEs), this article further develops novel theory and methodology for their macroscale mathematical/asymptotic homogenization. This article specifically encompasses the case of quasi-periodic heterogeneity with finite scale separation: no scale separation limit is required. A key innovation herein is to analyse the ensemble of all phase-shifts of the heterogeneity. Dynamical systems theory then frames the homogenization as a slow manifold of the ensemble. Depending upon any perceived scale separation within the quasi-periodic heterogeneity, the homogenization may be done in either one step or two sequential steps: the results are equivalent. The theory not only assures us of the existence and emergence of an exact homogenization at finite scale separation, it also provides a practical systematic method to construct the homogenization to any specified order. For a class of heterogeneities, we show that the macroscale homogenization is potentially valid down to lengths which are just twice that of the microscale heterogeneity! This methodology complements existing well-established results by providing a new rigorous and flexible approach to homogenization that potentially also provides correct macroscale initial and boundary conditions, treatment of forcing and control, and analysis of uncertainty.

Focusing on the late prehistoric southern Levant, we recently suggested that the diffused low-frequency distribution of large predator bones (lion, leopard and bear) coalesces into a coherent temporal pattern when observed at a sufficiently long timescale. While in the previous research we sought to determine what sort of sociocultural mechanism might explain this pattern, effectively drawing it into the orbit of the familiar, in this brief provocation, we push in the other direction, towards the unfamiliar: how can a process or phenomenon be culturally significant yet meaningless at the human and societal levels? How is a phenomenon substantial in the long term and insubstantial in the short term?

Waterhemp has evolved resistance to Group 2, 5, 9, 14, and 27 herbicides in Ontario, Canada, making control of this challenging weed even more difficult. Acetochlor is a Group 15, chloroacetanilide herbicide that has activity on many small-seeded annual grasses and some small-seeded annual broadleaf weeds, including waterhemp. The objective of this study was to ascertain if acetochlor mixtures with broadleaf herbicides (dicamba, metribuzin, diflufenican, sulfentrazone, or flumioxazin), applied preemergence (PRE), increase multiple-herbicide-resistant (MHR) waterhemp control in soybean. Five trials were conducted over 2 yr (2021 and 2022). The acetochlor mixtures caused ≤7% soybean injury, except acetochlor + flumioxazin, which caused 19% soybean injury. Acetochlor applied PRE controlled MHR waterhemp 89% at 4 wk after application (WAA). Dicamba, metribuzin, diflufenican, sulfentrazone, or flumioxazin controlled MHR waterhemp 59%, 67%, 58%, 64%, and 86%, respectively, at 4 WAA. Acetochlor applied in a mixture with dicamba, metribuzin, diflufenican, sulfentrazone, or flumioxazin provided good to excellent control of MHR waterhemp; control ranged from 91% to 98% but was similar to acetochlor applied alone. Acetochlor alone reduced MHR waterhemp density and biomass 98% and 93%; acetochlor + flumioxazin reduced waterhemp density and biomass by an additional 2% and 7%, respectively. This research concludes that acetochlor applied in a mixture with flumioxazin was the most efficacious mixture evaluated for MHR waterhemp control.

Fireweed (Senecio madagascariensis Poir.) has invaded and colonized numerous habitats in the coastal areas of southeastern Australia and is a major weed in cultivated lands as well as in poorly grassed, neglected, and highly grazed pastures. To examine the seed germination ecology of two populations (Felton and Gatton) of S. madagascariensis, experiments were conducted in the laboratory and screen house. The germination of both populations increased as the alternating temperatures increased from the coolest temperatures (15/5 C) to warmer temperatures (25/15 C). However, the highest temperature regime (35/25 C) resulted in the lowest germination rates. The Gatton population exhibited greater tolerance to higher temperatures, resulting in significantly higher germination (2.4 times) than the Felton population at the highest alternating temperature of 35/25 C. Compared with the Felton population, the Gatton population demonstrated higher tolerance to salt and water stress. In comparison to alternating light and dark periods (12 h each) (97% to 98%), the germination of both populations of S. madagascariensis was significantly reduced under complete darkness (24 h) (33% to 39%). A screen house seed burial depth experiment revealed similar emergence of S. madagascariensis seedlings between the populations. The maximum emergence (60%) was observed for seeds placed at the soil surface, followed by a dramatic decline in seedling emergence with an increase in depth. No seedlings emerged from a burial depth of 4 cm. With the addition of wheat (Triticum aestivum L.) crop residue to the soil surface at rates comparable to 4,000 to 8,000 kg ha−1, seedling emergence of S. madagascariensis decreased significantly. Information acquired from this study could be utilized to manage and develop effective weed management strategies for controlling S. madagascariensis in different agroecological conditions.

Soil amelioration via strategic deep tillage is occasionally utilized within conservation tillage systems to alleviate soil constraints, but its impact on weed seed burial and subsequent growth within the agronomic system is poorly understood. This study assessed the effects of different strategic deep-tillage practices, including soil loosening (deep ripping), soil mixing (rotary spading), or soil inversion (moldboard plow), on weed seed burial and subsequent weed growth, compared with a no-till control. The tillage practices were applied in 2019 at Yerecoin and Darkan, WA, and data on weed seed burial and growth were collected during the following 3-yr winter crop rotation (2019 to 2021). Soil inversion buried 89% of rigid ryegrass (Lolium rigidum Gaudin) and ripgut brome (Bromus diandrus Roth) seeds to a depth of 10 to 20 cm at both sites, while soil loosening and mixing left between 31% and 91% of the seeds in the top 0 to 10 cm of soil, with broad variation between sites. Few seeds were buried beyond 20 cm despite tillage working depths exceeding 30 cm at both sites. Soil inversion reduced the density of L. rigidum to <1 plant m−2 for 3 yr after strategic tillage. Bromus diandrus density was initially reduced to 0 to 1 plant m−2 by soil inversion, but increased to 4 plants m−2 at Yerecoin in 2020 and 147 plants at Darkan in 2021. Soil loosening or mixing did not consistently decrease weed density. The field data were used to parameterize a model that predicted weed density following strategic tillage with greater accuracy for soil inversion than for loosening or mixing. The findings provide important insights into the effects of strategic deep tillage on weed management in conservational agricultural systems and demonstrate the potential of models for optimizing weed management strategies.

During the third millennium BC, new types of anthropogenic landscape emerged across northern Europe: heathlands and pasture. These open landscapes afforded mobile pastoralism and the arena for a new funerary practice: barrow building. Here, the authors define this entanglement of people, animals and landscapes as a literal and figurative ‘ancestral commons’. Focusing on western Jutland, they combine palaeoecological and archaeological evidence to characterise the form and temporal depth of the co-emergent links between pastoralism, barrows and mobility. Conceptualising the ancestral commons as a deep-time entanglement, characterised by rhythms of physical and metaphorical movement, reveals a landscape that afforded shared understanding of the ancestral past and a foundation for the subsequent Nordic Bronze Age.

Plants are complex systems made up of many interacting components, ranging from architectural elements such as branches and roots, to entities comprising cellular processes such as metabolic pathways and gene regulatory networks. The collective behaviour of these components, along with the plant’s response to the environment, give rise to the plant as a whole. Properties that result from these interactions and cannot be attributed to individual parts alone are called emergent properties, occurring at different time and spatial scales. Deepening our understanding of plant growth and development requires computational tools capable of handling a large number of interactions and a multiscale approach connecting properties across scales. There currently exist few methods able to integrate models across scales, or models capable of predicting new emergent plant properties. This perspective explores current approaches to modelling emergent behaviour in plants, with a focus on how current and future tools can handle multiscale plant systems.

In this study, we propose an operationalization of the concept of emergence which plays a crucial role in usage-based theories of language. The abstractions linguists operate with are assumed to emerge through a process of generalization over the data language users are exposed to. Here, we use two types of computational learning algorithms that differ in how they formalize and execute generalization and, consequently, abstraction, to probe whether a type of language knowledge that resembles linguistic abstractions could emerge from exposure to raw data only. More specifically, we investigated whether a phone, undisputedly the simplest of all linguistic abstractions, could emerge from exposure to speech sounds using two computational learning processes: memory-based learning and error-correction learning (ECL). Both models were presented with a significant amount of pre-processed speech produced by one speaker. We assessed (1) the consistency or stability of what these simple models learn and (2) their ability to approximate abstract categories. Both types of models fare differently regarding these tests. We show that only ECL models can learn abstractions and that at least part of the phone inventory and its grouping into traditional types can be reliably identified from the input.

During the last two decades, the world has witnessed the emergence and re-emergence of arthropod-borne viruses, better known as arboviruses. The close contact between sylvatic, rural and peri-urban vector species and humans has been mainly determined by the environment-modifying human activity. The resulting interactions have led to multiple dead-end host infections and have allowed sylvatic arboviruses to eventually adapt to new vectors and hosts, contributing to the establishment of urban transmission cycles of some viruses with enormous epidemiologic impact. The metagenomic next-generation sequencing (NGS) approach has allowed obtaining unbiased sequence information of millions of DNA and RNA molecules from clinical and environmental samples. Robust bioinformatics tools have enabled the assembly of individual sequence reads into contigs and scaffolds partially or completely representing the genomes of the microorganisms and viruses being present in biological samples of clinical relevance. In this review, we describe the different ecological scenarios for the emergence of viral diseases, the virus adaptation process required for the establishment of a new transmission cycle and the usefulness of NGS and computational methods for the discovery and routine genomic surveillance of mosquito-borne viruses in their ecosystems.