Repeated use of xenobiotic chemicals has selected for the rapid evolution of resistance, threatening health and food security at a global scale. Strategies for preventing the evolution of resistance include cycling and mixtures of chemicals and diversification of management. We currently lack large-scale studies that evaluate the efficacy of these different strategies for minimizing the evolution of resistance. Here we use a national-scale data set of occurrence of the weed Alopecurus myosuroides (black-grass) in the United Kingdom to address this. Weed densities are correlated with assays of evolved resistance, supporting the hypothesis that resistance is driving weed abundance at a national scale. Resistance was correlated with the frequency of historical herbicide applications, suggesting that evolution of resistance is primarily driven by intensity of exposure to herbicides, but was unrelated directly to other cultural techniques. We find that populations resistant to one herbicide are likely to show resistance to multiple herbicide classes. Finally, we show that the economic costs of evolved resistance are considerable: loss of control through resistance can double the economic costs of weeds. This research highlights the importance of managing threats to food production and healthcare systems using an evolutionarily informed approach in a proactive not reactive manner.

Intense selection by pesticides and antibiotics has resulted in a global epidemic of evolved resistance. In agriculture and medicine, using mixtures of compounds from different classes is widely accepted as optimal resistance management. However, this strategy may promote the evolution of more generalist resistance mechanisms. Here we test this hypothesis at a national scale in an economically important agricultural weed: blackgrass (Alopecurus myosuroides), for which herbicide resistance is a major economic issue. Our results reveal that greater use of herbicide mixtures is associated with lower levels of specialist resistance mechanisms, but higher levels of a generalist mechanism implicated in enhanced metabolism of herbicides with diverse modes of action. Our results indicate a potential evolutionary trade-off in resistance management, whereby attempts to reduce selection for specialist resistance traits may promote the evolution of generalist resistance. We contend that where specialist and generalist resistance mechanisms co-occur, similar trade-offs will be evident, calling into question the ubiquity of resistance management based on mixtures and combination therapies.

Summary: Mapping weed densities within crops has conventionally been achieved either by detailed ecological monitoring or by field walking, both of which are time‐consuming and expensive. Recent advances have resulted in increased interest in using Unmanned Aerial Systems (UAS) to map fields, aiming to reduce labour costs and increase the spatial extent of coverage. However, adoption of this technology ideally requires that mapping can be undertaken automatically and without the need for extensive ground‐truthing. This approach has not been validated at large scale using UAS‐derived imagery in combination with extensive ground‐truth data. We tested the capability of UAS for mapping a grass weed, Alopecurus myosuroides, in wheat crops. We addressed two questions: (i) can imagery accurately measure densities of weeds within fields and (ii) can aerial imagery of a field be used to estimate the densities of weeds based on statistical models developed in other locations? We recorded aerial imagery from 26 fields using a UAS. Images were generated using both RGB and R_mod (R_mod 670–750 nm) spectral bands. Ground‐truth data on weed densities were collected simultaneously with the aerial imagery. We combined these data to produce statistical models that (i) correlated ground‐truth weed densities with image intensity and (ii) forecast weed densities in other fields. We show that weed densities correlated with image intensity, particularly R_mod image data. However, results were mixed in terms of out of sample prediction from field‐to‐field. We highlight the difficulties with transferring models and we discuss the challenges for automated weed mapping using UAS technology.

Transport infrastructure is a pervasive element in modern landscapes and continues to expand to meet the demands of a growing human population and its associated resource consumption. Road-induced mortality is often thought to be a major contributor to the marked declines of European hedgehog populations. This review synthesizes available evidence on the population-level impacts of road mortality and the threat to population viability for the five hedgehog species in Europe. Local and national studies suggest that road mortality can cause significant depletions in population sizes, predominantly removing adult males. Traffic collisions are a probable cause of fragmentation effects, subsequently undermining ecological processes such as dispersal, as well as the genetic variance and fitness of isolated populations. Further studies are necessary to improve population estimates and explicitly examine the consequences of sex- and age-specific mortality rates. Hedgehogs have been reported to use crossing structures, such as road tunnels, yet evaluations of mitigation measures for population survival probability are largely absent. This highlights the need for robust studies that consider population dynamics and genetics in response to mitigation. In light of ongoing declines of hedgehog populations, it is paramount that applied research is prioritised and integrated into a holistic spatial planning process.