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1. Pollinators experience large spatio-temporal fluctuations in resource availability when mass-flowering crops are rotated with resource-poor cereal crops. Yet, few studies have considered the effect this has on pollinator population stability, nor how this might be mitigated to maintain consistent crop pollination services.\ud 2. We assess the potential of boundary features (standard narrow 1m grassy margins, hedgerows and wide 4m agri-environment margins) to support and stabilise pollinator populations and pollination service in agricultural landscapes under crop rotation. Assuming a six-year rotation, we use a process-based pollinator model to predict yearly pollinator population size and in-crop visitation rates to oilseed rape and field bean across 117 study landscapes in England with varying amounts of boundary features. We model both ground-nesting bumblebees and solitary bees and compare the predictions including and excluding boundary features from the landscapes.\ud 3. Ground-nesting bumblebee populations, whose longer-lifetime colonies bene�t from continuity of resources, were larger and more stable (relative to the no-features scenario) in landscapes with more boundary features. Ground-nesting solitary bee populations were also larger but not significantly more stable, except with the introduction of wide permanent agri-environment margins, due to their shorter lifetimes and shorter foraging/dispersal ranges.\ud 4. Crop visitation by ground-nesting bumblebees was greater and more stable in landscapes with more boundary features, partly due to increased colony growth prior to crop \ud flowering. Time averaged crop visitation by ground-nesting solitary bees was slightly lower, due to females dividing their foraging time between boundary features and the crop, but the more stable delivery compensated for this by nonetheless increasing the minimum pollination service delivered in any given year.\ud 5. Synthesis and applications. Boundary features have an important role in stabilising pollinator populations and pollination service in rotational systems, although maintenance of larger semi-natural habitat patches may be more effective for stabilising less mobile solitary bee populations. We recommend using combinations of boundary features, accounting for pollinator range when spacing features/rotating crops, and synchronising boundary feature management with crop rotation to maximise their stabilising benefits.

1. Sustainable agriculture aims to produce sufficient food while minimizing environmental damage. To achieve this, we need to understand the role of agricultural landscapes in providing diverse ecosystem services and how these affect crop production and resilience, that is, maintaining yields despite environmental perturbation.\ud We used 10 years of English wheat yield data to derive three metrics of resilience (relative yield across the time series, yield stability around a moving average and resistance to an extreme weather event) at 10 km × 10 km resolution. We used remotely sensed maps to calculate measures of landscape structure, including composition (proportions of different land cover types) and configuration (metrics of connectivity and proximity), known to affect ecosystem service delivery (e.g. control of pests by beneficial invertebrates). We then used an information‐theoretic approach to identify the best‐fitting combination of landscape structure predictors for each resilience metric, using a potential yield model to account for the effects of climate and soils.\ud Relative yield showed a strongly positive relationship with the area of arable land. For yield stability, this relationship was evident but alongside other landscape structure variables in the best‐fitting model. No relationship with arable land was evident for resistance.\ud Yield stability showed a strongly positive effect of proximity to semi‐natural habitats. For resistance, the best‐fitting model included positive relationships with the cover of semi‐natural habitats and proximity to semi‐natural grasslands.\ud Synthesis and applications. Landscapes with the highest relative wheat yields did not show the highest yield stability or resistance to extreme events. As resilience metrics were derived from shorter portions of the time series, the importance of semi‐natural habitats compared to arable land increased. This is probably driven by the complex interplay between landscape structure, agricultural management and ecosystem services. These results demonstrate that measuring relative yield over time may be insufficient to capture the full effect that non‐arable components of the landscape, and the ecosystem services they deliver, have on other aspects of resilience, and that there are clear trade‐offs in managing agricultural landscapes to maximize different aspects of crop yield resilience.

1) Dispersal ability is key to species persistence in times of environmental change. Assessing a species’ vulnerability and response to anthropogenic changes is often performed using one of two methods: correlative approaches that infer dispersal potential based on traits, such as wingspan or an index of mobility derived from expert opinion, or a mechanistic modelling approach that extrapolates displacement rates from empirical data on short-term movements. \ud 2) Here we compare and evaluate the success of the correlative and mechanistic approaches using a mechanistic random-walk model of butterfly movement that incorporates relationships between wingspan and sex-specific movement behaviours. \ud 3) The model was parameterised with new data collected on four species of butterfly in the South of England and we observe how wingspan relates to flight speeds, turning angles, flight durations, and displacement rates.\ud 4) We show that flight speeds and turning angles correlate with wingspan but that to achieve good prediction of displacement even over 10 minutes the model must also include details of sex- and species-specific movement behaviours. \ud 5) We discuss what factors are likely to differentially motivate the sexes and how these could be included in mechanistic models of dispersal to improve their use in ecological forecasting.

The intensification of agricultural practices throughout the twentieth century has had large detrimental effects on biodiversity and these are likely to increase as the human population rises, with consequent pressure on land. To offset these negative impacts, agri-environment schemes have been widely implemented, offering financial incentives for land-owners to create or maintain favourable habitats that enhance or maintain biodiversity. While some evidence is available on the resulting species richness and abundance for groups such as natural predators, pollinating insects including butterflies and moths, this is costly to obtain and it is difficult to predict the effects of specific habitat designs. To alleviate this problem we here develop an individual-based model (IBM), modelling the detailed movement behaviour, foraging, and energy budget of a grassland butterfly Maniola jurtina Linn. in patches of varying dimensions and quality. The IBM is successfully validated against data on M. jurtina densities, movement behaviour, resource use, fecundity and lifespan in habitats of varying quality. We use the IBM to quantify the benefits for life-history outcomes of M. jurtina of increasing the quantity and the quality of field margins within agricultural landscapes. We find that increasing the quantity of field margin habitat from 1 to 3 ha per 100 ha, as recommended in agri-environment schemes, increases the average number of eggs laid across a two-week period by 60% and adds an extra day to the average lifespan. Similar effects are reported for variation in the quality of field margins. We discuss the implications of the result for modelling butterfly responses to management scenarios.

Understanding how environmental change affects ecosystem function delivery is of primary importance for fundamental and applied ecology. Current approaches focus on single environmental driver effects on communities, mediated by individual response traits. Data limitations present constraints in scaling up this approach to predict the impacts of multivariate environmental change on ecosystem functioning. We present a more holistic approach to determine ecosystem function resilience, using long‐term monitoring data to analyze the aggregate impact of multiple historic environmental drivers on species' population dynamics. By assessing covariation in population dynamics between pairs of species, we identify which species respond most synchronously to environmental change and allocate species into “response guilds.” We then use “production functions” combining trait data to estimate the relative roles of species to ecosystem functions. We quantify the correlation between response guilds and production functions, assessing the resilience of ecosystem functioning to environmental change, with asynchronous dynamics of species in the same functional guild expected to lead to more stable ecosystem functioning. Testing this method using data for butterflies collected over four decades in the United Kingdom, we find three ecosystem functions (resource provisioning, wildflower pollination, and aesthetic cultural value) appear relatively robust, with functionally important species dispersed across response guilds, suggesting more stable ecosystem functioning. Additionally, by relating genetic distances to response guilds we assess the heritability of responses to environmental change. Our results suggest it may be feasible to infer population responses of butterflies to environmental change based on phylogeny—a useful insight for conservation management of rare species with limited population monitoring data. Our approach holds promise for overcoming the impasse in predicting the responses of ecosystem functions to environmental change. Quantifying co‐varying species' responses to multivariate environmental change should enable us to significantly advance our predictions of ecosystem function resilience and enable proactive ecosystem management., Current approaches to understand how environmental change affects ecosystem function delivery have reached an impasse due to data limitations and scaling constraints. Here, we present a more holistic approach using long‐term monitoring data to analyze the aggregate impact of multiple historic environmental drivers on species' population dynamics and what effects these have on ecosystem functioning. Quantifying co‐varying species' responses to multivariate environmental change should enable us to significantly advance our predictions of ecosystem function resilience and to enable proactive ecosystem management.