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12 pages, 6 figures, 2 tables, supporting information .-- Data Availability Statement: Raw sequence data are available on the Sequence Read Archive at PRJNA763761. Data available via the Dryad Digital Repository https://doi.org/10.5061/dryad.rjdfn2z9s (Lowe et al., 2022). All code is available at https://github.com/colford/nbgw-plant-illumina-pipeline, Gardens are important habitats for pollinators, providing floral resources and nesting sites. There are high levels of public support for growing ‘pollinator-friendly’ plants but while plant recommendation lists are available, they are usually inconsistent, poorly supported by scientific research and target a narrow group of pollinators. In order to supply the most appropriate resources, there is a clear need to understand foraging preferences, for a range of pollinators, across the season within horticultural landscapes. Using an innovative DNA metabarcoding approach, we investigated foraging preferences of four groups of pollinators in a large and diverse, horticultural and agricultural landscape, across the flowering season and over 2 years, significantly improving on the spatial and temporal scale that can be achieved using observational studies. Bumblebees, honeybees, non-corbiculate bees and hoverflies visited 191 plant taxa. Overall floral resources were shared between the different types of pollinators, but significant differences were seen between the plants used most abundantly by bees (Hymenoptera) and hoverflies (Diptera). Floral resource use by pollinators is strongly associated with seasonal changes in flowering plants, with pollinators relying on dominant plants found within each season, with preferences consistent across both years. The plants identified were categorised according to their native status to investigate the value of native and non-native plants. The majority of floral resources used were of native and near-native origin, but the proportion of horticultural and naturalised plants increased during late summer and autumn. Synthesis and applications. Plant recommendation lists for pollinators should distinguish between bees and hoverflies and provide evidence-based floral recommendations throughout the year that include native as well as non-native plants for use in the United Kingdom and Northern Europe. Specific management recommendations include reducing mowing to encourage plants such as dandelion Taraxacum officinale and buttercups Ranunculus spp., and reducing scrub management to encourage bramble Rubus fruticosus, N.d.V., L.J. and A.L. have received funding through the Welsh Government Rural Communities – Rural Development Programme 2014–2020, which is funded by the European Agricultural Fund for Rural Development and the Welsh Government. A.L. was supported by a Knowledge Economy Skills Scholarship (KESS2), part-funded by the Welsh Government's European Social Fund (ESF). Pollinator icons contained in the plant recommendation list were created by Thomas McBride. We acknowledge the support of the Supercomputing Wales project, which is part-funded by the European Regional Development Fund (ERDF) via Welsh Government, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

12 pages, 6 figures, supplementary information https://doi.org/10.1038/s41396-023-01370-8.-- Data Availability: Transcriptome sequencing data are available at the National Center for Biotechnology Information (NCBI) with the accession number PRJNA686847, Ocean warming (OW) and acidification (OA) are recognized as two major climatic conditions influencing phytoplankton growth and nutritional or toxin content. However, there is limited knowledge on the responses of harmful algal bloom species that produce toxins. Here, the study provides quantitative and mechanistic understanding of the acclimation and adaptation responses of the domoic acid (DA) producing diatom Pseudo-nitzschia multiseries to rising temperature and pCO2 using both a one-year in situ bulk culture experiment, and an 800-day laboratory acclimation experiment. Ocean warming showed larger selective effects on growth and DA metabolism than ocean acidification. In a bulk culture experiment, increasing temperature +4 °C above ambient seawater temperature significantly increased DA concentration by up to 11-fold. In laboratory when the long-term warming acclimated samples were assayed under low temperatures, changes in growth rates and DA concentrations indicated that P. multiseries did not adapt to elevated temperature, but could instead rapidly and reversibly acclimate to temperature shifts. However, the warming-acclimated lines showed evidence of adaptation to elevated temperatures in the transcriptome data. Here the core gene expression was not reversed when warming-acclimated lines were moved back to the low temperature environment, which suggested that P. multiseries cells might adapt to rising temperature over longer timescales. The distinct strategies of phenotypic plasticity to rising temperature and pCO2 demonstrate a strong acclimation capacity for this bloom-forming toxic diatom in the future ocean, This work was supported by Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) (No.2021QNLM050103-1), National Natural Science Foundation of China (41976110; 32072329; 32000404); the Young Taishan Scholars Program to DX, Taishan Scholars Funding; Central Public-interest Scientific Institution Basal Research Fund, CAFS (NO. 2020TD27, 20603022021019); Natural Science Foundation of Shandong Province (ZR2021MD075); Laoshan Laboratory (LSKJ202203204); China Agriculture Research System (CARS-50); U.S. California Urban Ocean Sea Grant and National Science Foundation grants (OCE 1638804, OCE 1538525, OCE 2149837, OCE 2120619), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

Generalist species are core components of ecological networks and crucial for the maintenance of biodiversity. Generalist species and networks are expected to be more resilient, and therefore understanding the dynamics of specialization and generalization in ecological networks is a key focus in a time of rapid global change. Whilst diet generalization is frequently studied, our understanding of how it changes over time is limited. Here we explore temporal variation in diet specificity in the honeybee (Apis mellifera), using pollen DNA metabarcoding of honey samples, through the foraging season, over two years. We find that, overall, honeybees are generalists that visit a wide range of plants, but there is temporal variation in the degree of specialization. Temporal specialization of honeybee colonies corresponds to periods of resource limitation, identified as a lack of honey stores. Honeybees experience a lack of preferred resources in June when switching from flowering trees in spring to shrubs and herbs in summer. Investigating temporal patterns in specialization can identify periods of resource limitation that may lead to species and network vulnerability. Diet specificity must therefore be explored at different temporal scales in order to fully understand species and network stability in the face of ecological change.

3 pages, 1 figure, Over the last two decades, there has been a huge increase in our understanding of microbial diversity, structure and composition enabled by high-throughput sequencing technologies. Yet, it is unclear how the number of sequences translates to the number of cells or species within the community. In some cases, additional observational data may be required to ensure relative abundance patterns from sequence reads are biologically meaningful. The goal of DNA-based methods for biodiversity assessments is to obtain robust community abundance data, simultaneously, from environmental samples. In this issue of Molecular Ecology Resources, Pierella Karlusich et al. (2022) describe a new method for quantifying phytoplankton cell abundance. Using Tara Oceans data sets, the authors propose the photosynthetic gene psbO for reporting accurate relative abundance of the entire phytoplankton community from metagenomic data. The authors demonstrate higher correlations with traditional optical methods (including microscopy and flow cytometry), using their new method, improving upon molecular abundance assessments using multicopy marker genes. Furthermore, to facilitate application of their approach, the authors curated a psbO gene database for accessible taxonomic queries. This is an important step towards improving species abundance estimates from molecular data and eventually reporting of absolute species abundance, enhancing our understanding of community dynamics, GLB received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 895338, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

Decreasing floral resources as a result of habitat loss is one of the key factors in the decline of pollinating insects worldwide. Understanding which plants pollinators use is vital to inform the provision of appropriate floral resources to help prevent pollinator loss. Using a globally important pollinator, the honeybee, we show how changes in agricultural intensification, crop use and the spread of invasive species, have altered the nectar and pollen sources available in the UK. Using DNA metabarcoding, we analysed 441 honey samples from 2017 and compared these to a nationwide survey of honey samples from 1952. We reveal that shifts in major plants foraged by honeybees are driven by changes in the availability of these plants within the landscape. Improved grasslands are the most widespread habitat type in the UK, and management changes within this habitat have the greatest potential to increase floral resource availability., Laura Jones et al. compare honey samples across the UK from 2017 to a nationwide survey of honey samples from 1952 in order to determine how nectar and pollen sources have changed over time. They find that shifts in major plants foraged by honeybees are driven by changes in the availability of these plants within the landscape, and focus on improved grasslands as the most widespread habitat type and a potential target for management efforts to increase floral resource availability.

Availability of suitable nectar and pollen resources is a limiting factor for pollinator survival, with both overall resource quantity and quality, along with provision throughout the season, being critical. Yet, our understanding of how the selection of floral resources changes over time, and how this relates to floral availability within the landscape is limited. To fill this knowledge gap, we characterise floral use by honeybee (Apis mellifera) colonies, in a diverse horticultural and agricultural landscape, from April to September, over two years, using pollen DNA metabarcoding of honey samples. We compared the pollen found to the availability of host plants within the surrounding landscape using floral surveys. Honeybees used a total of 143 plant taxa, but only 10 of these were determined as major sources (>10% of total sequence reads in any month) and total plant use represented a small proportion of the available floral resources (23% of genera). Distinct patterns of diet specialisation were identified in June and August, where colonies diverged in their floral preferences before re-aligning in July and September. Following optimal foraging theory, these patterns represent periods of resource limitation. Honeybees showed a preference for flowering trees in the spring, followed by shrubs and herbs in summer and used native and near-native plants more than horticultural plants, as major food sources. DNA metabarcoding allows an increased ecological insight into floral resource use by honeybees and highlights the importance of providing continuous and sufficient floral resources throughout the year.

Hay fever patients can be helped in managing their symptoms by predicting the severity of the upcoming grass pollen seasons., Allergic rhinitis is an inflammation in the nose caused by overreaction of the immune system to allergens in the air. Managing allergic rhinitis symptoms is challenging and requires timely intervention. The following are major questions often posed by those with allergic rhinitis: How should I prepare for the forthcoming season? How will the season’s severity develop over the years? No country yet provides clear guidance addressing these questions. We propose two previously unexplored approaches for forecasting the severity of the grass pollen season on the basis of statistical and mechanistic models. The results suggest annual severity is largely governed by preseasonal meteorological conditions. The mechanistic model suggests climate change will increase the season severity by up to 60%, in line with experimental chamber studies. These models can be used as forecasting tools for advising individuals with hay fever and health care professionals how to prepare for the grass pollen season.

Grass (Poaceae) pollen is the most important outdoor aeroallergen,1 exacerbating a range of respiratory conditions, including allergic asthma and rhinitis ("hay fever").2-5 Understanding the relationships between respiratory diseases and airborne grass pollen with a view to improving forecasting has broad public health and socioeconomic relevance. It is estimated that there are over 400 million people with allergic rhinitis6 and over 300 million with asthma, globally,7 often comorbidly.8 In the UK, allergic asthma has an annual cost of around US$ 2.8 billion (2017).9 The relative contributions of the >11,000 (worldwide) grass species (C. Osborne et al., 2011, Botany Conference, abstract) to respiratory health have been unresolved,10 as grass pollen cannot be readily discriminated using standard microscopy.11 Instead, here we used novel environmental DNA (eDNA) sampling and qPCR12-15 to measure the relative abundances of airborne pollen from common grass species during two grass pollen seasons (2016 and 2017) across the UK. We quantitatively demonstrate discrete spatiotemporal patterns in airborne grass pollen assemblages. Using a series of generalized additive models (GAMs), we explore the relationship between the incidences of airborne pollen and severe asthma exacerbations (sub-weekly) and prescribing rates of drugs for respiratory allergies (monthly). Our results indicate that a subset of grass species may have disproportionate influence on these population-scale respiratory health responses during peak grass pollen concentrations. The work demonstrates the need for sensitive and detailed biomonitoring of harmful aeroallergens in order to investigate and mitigate their impacts on human health.

Grass pollen is the world’s most harmful outdoor aeroallergen and sensitivity varies between species. Different species of grass flower at different times, but it is not known how airborne communities of grass pollen change in time and space. Persistence and high mobility of grass pollen could result in increasingly diverse seasonal pollen communities. Conversely, if grass pollen does not persist for an extended time in the air, shifting pollen communities would be predicted throughout the summer months. Here, using targeted high throughput sequencing, we tracked the seasonal progression of airborne Poaceae pollen biodiversity across Britain, throughout the grass allergy season. All grass genera displayed discrete, temporally restricted peaks of pollen incidence which varied with latitude, revealing that the taxonomic composition of grass pollen exposure changes substantially across the allergy season. By developing more refined aeroallergen profiling, we predict that our findings will facilitate the exploration of links between taxon-specific exposure of harmful grass pollen and disease, with concomitant socio-economic benefits.

Kelp are main iodine accumulators in the ocean, and their growth and photosynthesis are likely to benefit from elevated seawater CO2 levels due to ocean acidification. However, there are currently no data on the effects of ocean acidification on iodine metabolism in kelp. As key primary producers in coastal ecosystems worldwide, any change in their iodine metabolism caused by climate change will potentially have important consequences for global geochemical cycles of iodine, including iodine levels of coastal food webs that underpin the nutrition of billions of humans around the world. Here, we found that elevated pCO2 enhanced growth and increased iodine accumulation not only in the model kelp Saccharina japonica using both short-term laboratory experiment and long-term in situ mesocosms, but also in several other edible and ecologically significant seaweeds using long-term in situ mesocosms. Transcriptomic and proteomic analysis of S. japonica revealed that most vanadium-dependent haloperoxidase genes involved in iodine efflux during oxidative stress are down-regulated under increasing pCO2 , suggesting that ocean acidification alleviates oxidative stress in kelp, which might contribute to their enhanced growth. When consumed by abalone (Haliotis discus), elevated iodine concentrations in S. japonica caused increased iodine accumulation in abalone, accompanied by reduced synthesis of thyroid hormones. Thus, our results suggest that kelp will benefit from ocean acidification by a reduction in environmental stress however; iodine levels, in kelp-based coastal food webs will increase, with potential impacts on biogeochemical cycles of iodine in coastal ecosystems.

Successfully predicting the future states of systems that are complex, stochastic, and potentially chaotic is a major challenge. Model forecasting error (FE) is the usual measure of success; however model predictions provide no insights into the potential for improvement. In short, the realized predictability of a specific model is uninformative about whether the system is inherently predictable or whether the chosen model is a poor match for the system and our observations thereof. Ideally, model proficiency would be judged with respect to the systems’ intrinsic predictability, the highest achievable predictability given the degree to which system dynamics are the result of deterministic vs. stochastic processes. Intrinsic predictability may be quantified with permutation entropy (PE), a model‐free, information‐theoretic measure of the complexity of a time series. By means of simulations, we show that a correlation exists between estimated PE and FE and show how stochasticity, process error, and chaotic dynamics affect the relationship. This relationship is verified for a data set of 461 empirical ecological time series. We show how deviations from the expected PE–FE relationship are related to covariates of data quality and the nonlinearity of ecological dynamics. These results demonstrate a theoretically grounded basis for a model‐free evaluation of a system's intrinsic predictability. Identifying the gap between the intrinsic and realized predictability of time series will enable researchers to understand whether forecasting proficiency is limited by the quality and quantity of their data or the ability of the chosen forecasting model to explain the data. Intrinsic predictability also provides a model‐free baseline of forecasting proficiency against which modeling efforts can be evaluated., Ecological Monographs, 89 (2), ISSN:0012-9615, ISSN:1557-7015, ISSN:1741-7015

Shifts in the growth rate of a model green alga cultured in the presence of one or a combination of up to eight environmental drivers can generally be explained by the response to a single dominant driver, such as pH or temperature.

Climate change is altering aquatic environments in a complex way, and simultaneous shifts in many properties will drive evolutionary responses in primary producers at the base of both freshwater and marine ecosystems. So far, evolutionary studies have shown how changes in environmental drivers, either alone or in pairs, affect the evolution of growth and other traits in primary producers. Here, we evolve a primary producer in ninety-six unique environments with different combinations of between one and eight environmental drivers to understand how evolutionary responses to environmental change depend on the identity and number of drivers. Even in multidriver environments, only a few dominant drivers explain most of the evolutionary changes in population growth rates. Most populations converge on the same growth rate by the end of the evolution experiment. However, populations adapt more when these dominant drivers occur in the presence of other drivers. This is due to an increase in the intensity of selection in environments with more drivers, which are more likely to include dominant drivers. Concurrently, many of the trait changes that occur during the initial short-term response to both single and multidriver environmental change revert after about 450 generations of evolution. In future aquatic environments, populations will encounter differing combinations of drivers and intensities of selection, which will alter the adaptive potential of primary producers. Accurately gauging the intensity of selection on key primary producers will help in predicting population size and trait evolution at the base of aquatic food webs.