Darren M. Evans

Management of insect pests of oilseed rape ( Brassica napus L.) such as cabbage stem flea beetle (CSFB; Psylliodes chrysocephala L.) is increasingly challenging given a lack of effective permitted insecticides. Conservation biocontrol, including through naturally-occurring parasitoids of CSFB, is one of the first lines of defense and the baseline for integrated pest management (IPM) strategies, but we lack a comprehensive understanding of the parasitoids involved and how parasitism rates vary regionally. Such data have traditionally required rearing of parasitoids from their pest hosts, which is highly error-prone, laborious and overlooks instances of multiple parasitism and hyperparasitism. Using multi-marker DNA metabarcoding, including a novel host-exclusion parasitoid PCR primer pair, we assessed parasitism of 1,040 CSFB larvae from 13 counties in England, UK. We confidently identified seven parasitoids of CSFB larvae, with ten taxa present in unfiltered data. Parasitism rates differed between regions, with the north of England having significantly lower parasitism rates than southern regions. Our novel beetle parasitoid PCR primers demonstrate a high congruence with visual detections of parasitoids and a high rate of novel interaction detections. Ultimately, by determining both rates of parasitism and natural enemy identities, these data significantly extend our understanding of in-field CSFB parasitoid community dynamics. This information could guide the enhancement of farm management, especially using network science in decision support systems related to insecticide application, but also more broadly in the context of crop and habitat management for improved conservation biocontrol and IPM.

Understanding how large herbivores influence cyclic small-mammal populations is essential for predicting community dynamics in grazed ecosystems. Using a 23-year upland grazing experiment in Scotland, United Kingdom, we test how varying livestock grazing pressures affect population cycles of the short-tailed field vole (Microtus agrestis), a keystone herbivore and prey species of birds of conservation concern. We combined long-term vole sign indices (VSI), vegetation surveys, climate data and used wavelet analyses and mixed-effects models to quantify changes in VSI (a proxy for abundance), cycle amplitude and periodicity across four grazing treatments and three spatial blocks. Vole abundance and cycle amplitude declined with increasing grazing pressure, yet the frequency of vole population oscillations remained strikingly consistent across all treatments, with a dominant ~3.5-year cycle persisting throughout most of the study. Ungrazed plots supported the highest mean VSI and the largest amplitude oscillations. We found a pronounced, spatially-restricted cycle collapse occurred between 2009-2016 in one experimental block. This breakdown coincided with unusually cold winters and lower vegetation density, suggesting that local microclimate and habitat structure interact to destabilise vole cycles. Vegetation density (but not height) predicted VSI in the more intensively grazed treatments, indicating that grazing-driven changes in habitat structure indirectly mediate vole dynamics. Our findings demonstrate that large herbivores can substantially modify vole population dynamics through both direct and indirect pathways, altering cycle amplitude, habitat suitability and resilience to climatic perturbations. These results highlight the need to consider interactions between grazing, vegetation structure and climate variability when predicting small-mammal population dynamics and their cascading ecological effects.

Understanding species interactions is critical for ecology and conservation, yet traditional monitoring methods often lack spatiotemporal resolution and important contextual information. The growing field of ecoacoustics enables remote sensing across large spatiotemporal scales and the monitoring of otherwise cryptic communities. This provides direct and indirect evidence of interactions by detecting characteristic acoustic signatures. Investigating species-interactions through ecoacoustics presents a unique opportunity to enhance the construction, inference, and interpretation of ecological networks, advancing the frontier of next-generation biomonitoring. A synthesis on the application of ecoacoustics to the study of ecological interactions is, however, lacking, hindering adoption of this approach across a broader range of contexts. This review explores the potential of ecoacoustics to detect and infer predator-prey interactions across a diverse range of contexts. We outline how individual interactions can be detected from specific acoustic signatures like feeding sounds, alarm calls and shifts in prey behaviour, each supported by case studies from the literature. When used in conjunction with other methods, ecoacoustics can provide contextual information that can for guide inference of trophic networks by approximating structural properties of networks. Ecoacoustics ultimately offers a scalable, cost-effective tool for identifying interactions, and monitoring and constructing trophic networks across a diverse range of ecological contexts, for which we envisage this article providing a foundation.

Just over two decades ago, DNA-based dietary analysis promised to advance the resolution, sensitivity and speed with which we could detect and identify trophic interactions. Since then, DNA-based dietary analyses have generated a paradigm shift in our understanding of trophic interactions across a wide range of natural systems. With decreasing sequencing costs and increased accessibility to sequencing technologies, adoption has broadened significantly in recent years, but advances to the practical approaches used for dietary analysis have arguably slowed. We stand now, however, at the precipice of the next advance as traditional DNA-based ecological studies increasingly apply RNA-based methods. To date, this has most commonly taken place in the context of environmental monitoring, and the application to dietary analysis is still underrepresented despite immense potential and relatively straightforward implementation. Given the increased sensitivity of RNA, the detection of prey material via RNA can mitigate many longstanding methodological pitfalls of DNA-based dietary analyses, by (i) differentiating between carrion and live prey, (ii) identifying potential false positives, and (iii) providing temporal context to detections, facilitating construction of weighted or multilayer trophic networks. Functional RNA, detected via transcriptomics, may also present an opportunity to ascribe functional context to both predator and prey, and contextualise interactions or their impact on wider trophic networks. With increasing accessibility to RNA sequencing, the application of these methods to community and network ecology may significantly advance our understanding of the drivers of trophic interactions in complex natural systems. Here, we summarise RNA-based dietary analysis methods that can be applied to dietary analysis with reference to recent literature in trophic ecology and relevant applications.

Feeding a growing human population while preventing biodiversity loss is a major challenge. Land conversion impacts multiple ecosystem services (ESs), including food production and biodiversity-dependent services; yet, the role of indirect effects on ESs within this context, such as parasitoids boosting crop yield by controlling herbivores, remains poorly understood. Using species-network data from an organic agroecosystem with multiple habitats, we simulated the effects of converting extensive to intensive crop production on multiple ESs. Projected land conversion increased crop yield by up to 191% but severely reduced other ESs (e.g., pollination by 95%). However, indirect effects on ES-providing species declined by 97%, revealing undescribed effects of habitat conversion. Comparison to a null model showed that the identity of species lost either mitigates or amplifies these effects, depending on the ES type. Uncovering how land-use changes shape direct and indirect interplay among multiple services is crucial for sustainable agroecosystem management.

Insects play a vital role in ecosystem functioning, but in some parts of the world, their populations have declined significantly in recent decades due to environmental change, agricultural intensification and other anthropogenic drivers. Monitoring insect populations is crucial for understanding and mitigating biodiversity loss, especially in agro‐ecosystems where a focus on pests and beneficial insects is gaining momentum in the context of sustainable food systems. Biomonitoring has long played an important role in providing early warnings of insect pests and their vectored pathogens and for assessing agro‐ecosystem management. However, identification of invertebrates by taxonomists is time‐consuming and fraught with numerous other challenges, particularly when it comes to real‐time monitoring. Recent technological advances in both computational image recognition and molecular ecology have enhanced biomonitoring efficiency and accuracy, reducing labour efforts, but leading to unprecedented volumes of data generated. This perspective article examines the significance and further potential of deep learning with image‐based recognition, aided by complementary technologies, in advancing entomological biomonitoring. Using entomological sticky traps as an example, we discuss each step of the workflow required to create ecological networks using image‐based recognition, multimodal data and deep learning, and we identify the challenges inherent to this method and other insect survey techniques. In order to become mainstream for global biomonitoring, access to long‐term, standardised multimodal data is required for comprehending ecosystem dynamics, structure and function and for mitigating insect population declines.

Nutrients can shape ecological interactions but remain poorly integrated into ecological networks. Concepts such as nutrient‐specific foraging nevertheless have the potential to expose the mechanisms structuring complex ecological systems. Nutrients also present an opportunity to predict dynamic processes, such as interaction rewiring and extinction cascades, and increase the accuracy of network analyses. Here, we propose the concept of nutritional networks. By integrating nutritional data into ecological networks, we envisage significant advances to our understanding of ecological processes from individual to ecosystem scales. We show that networks can be constructed with nutritional data to illuminate how nutrients structure ecological interactions in natural systems through an empirical example. Throughout, we identify fundamental ecological hypotheses that can be explored in a nutritional network context, alongside methods for resolving those networks. Nutrients influence the structure and complexity of ecological networks through mechanistic processes and concepts including nutritional niche differentiation, functional responses, landscape diversity, ecological invasions and ecosystem robustness. Future research on ecological networks should consider nutrients when investigating the drivers of network structure and function.