Immune Response and Pathogen Invasion at the Choroid Plexus in the Onset of Cerebral Toxoplasmosis

BACKGROUND: Toxoplasma gondii (T. gondii) is a highly successful parasite being able to cross all biological barriers of the body, finally reaching the central nervous system (CNS). Previous studies have highlighted the critical involvement of the blood–brain barrier (BBB) during T. gondii invasion and development of subsequent neuroinflammation. Still, the potential contribution of the choroid plexus (CP), the main structure forming the blood–cerebrospinal fluid (CSF) barrier (BCSFB) have not been addressed. METHODS: To investigate T. gondii invasion at the onset of neuroinflammation, the CP and brain microvessels (BMV) were isolated and analyzed for parasite burden. Additionally, immuno-stained brain sections and three-dimensional whole mount preparations were evaluated for parasite localization and morphological alterations. Activation of choroidal and brain endothelial cells were characterized by flow cytometry. To evaluate the impact of early immune responses on CP and BMV, expression levels of inflammatory mediators, tight junctions (TJ) and matrix metalloproteinases (MMPs) were quantified. Additionally, FITC-dextran was applied to determine infection-related changes in BCSFB permeability. Finally, the response of primary CP epithelial cells to T. gondii parasites was tested in vitro. RESULTS: Here we revealed that endothelial cells in the CP are initially infected by T. gondii, and become activated prior to BBB endothelial cells indicated by MHCII upregulation. Additionally, CP elicited early local immune response with upregulation of IFN-γ, TNF, IL-6, host-defence factors as well as swift expression of CXCL9 chemokine, when compared to the BMV. Consequently, we uncovered distinct TJ disturbances of claudins, associated with upregulation of MMP-8 and MMP-13 expression in infected CP in vivo, which was confirmed by in vitro infection of primary CP epithelial cells. Notably, we detected early barrier damage and functional loss by increased BCSFB permeability to FITC-dextran in vivo, which was extended over the infection course. CONCLUSIONS: Altogether, our data reveal a close interaction between T. gondii infection at the CP and the impairment of the BCSFB function indicating that infection-related neuroinflammation is initiated in the CP. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12974-021-02370-1

Elective surgery system strengthening: development, measurement, and validation of the surgical preparedness index across 1632 hospitals in 119 countries

Background The 2015 Lancet Commission on global surgery identified surgery and anaesthesia as indispensable parts of holistic health-care systems. However, COVID-19 exposed the fragility of planned surgical services around the world, which have also been neglected in pandemic recovery planning. This study aimed to develop and validate a novel index to support local elective surgical system strengthening and address growing backlogs. Methods First, we performed an international consultation through a four-stage consensus process to develop a multidomain index for hospital-level assessment (surgical preparedness index; SPI). Second, we measured surgical preparedness across a global network of hospitals in high-income countries (HICs), middle-income countries (MICs), and low-income countries (LICs) to explore the distribution of the SPI at national, subnational, and hospital levels. Finally, using COVID-19 as an example of an external system shock, we compared hospitals' SPI to their planned surgical volume ratio (SVR; ie, operations for which the decision for surgery was made before hospital admission), calculated as the ratio of the observed surgical volume over a 1-month assessment period between June 6 and Aug 5, 2021, against the expected surgical volume based on hospital administrative data from the same period in 2019 (ie, a pre-pandemic baseline). A linear mixed-effects regression model was used to determine the effect of increasing SPI score. Findings In the first phase, from a longlist of 103 candidate indicators, 23 were prioritised as core indicators of elective surgical system preparedness by 69 clinicians (23 [33%] women; 46 [67%] men; 41 from HICs, 22 from MICs, and six from LICs) from 32 countries. The multidomain SPI included 11 indicators on facilities and consumables, two on staffing, two on prioritisation, and eight on systems. Hospitals were scored from 23 (least prepared) to 115 points (most prepared). In the second phase, surgical preparedness was measured in 1632 hospitals by 4714 clinicians from 119 countries. 745 (45·6%) of 1632 hospitals were in MICs or LICs. The mean SPI score was 84·5 (95% CI 84·1–84·9), which varied between HIC (88·5 [89·0–88·0]), MIC (81·8 [82·5–81·1]), and LIC (66·8 [64·9–68·7]) settings. In the third phase, 1217 (74·6%) hospitals did not maintain their expected SVR during the COVID-19 pandemic, of which 625 (51·4%) were from HIC, 538 (44·2%) from MIC, and 54 (4·4%) from LIC settings. In the mixed-effects model, a 10-point increase in SPI corresponded to a 3·6% (95% CI 3·0–4·1; p<0·0001) increase in SVR. This was consistent in HIC (4·8% [4·1–5·5]; p<0·0001), MIC (2·8 [2·0–3·7]; p<0·0001), and LIC (3·8 [1·3–6·7%]; p<0·0001) settings. Interpretation The SPI contains 23 indicators that are globally applicable, relevant across different system stressors, vary at a subnational level, and are collectable by front-line teams. In the case study of COVID-19, a higher SPI was associated with an increased planned surgical volume ratio independent of country income status, COVID-19 burden, and hospital type. Hospitals should perform annual self-assessment of their surgical preparedness to identify areas that can be improved, create resilience in local surgical systems, and upscale capacity to address elective surgery backlogs. Funding National Institute for Health Research (NIHR) Global Health Research Unit on Global Surgery, NIHR Academy, Association of Coloproctology of Great Britain and Ireland, Bowel Research UK, British Association of Surgical Oncology, British Gynaecological Cancer Society, and Medtronic.publishedVersio

Elective surgery system strengthening: development, measurement, and validation of the surgical preparedness index across 1632 hospitals in 119 countries

Background: The 2015 Lancet Commission on global surgery identified surgery and anaesthesia as indispensable parts of holistic health-care systems. However, COVID-19 exposed the fragility of planned surgical services around the world, which have also been neglected in pandemic recovery planning. This study aimed to develop and validate a novel index to support local elective surgical system strengthening and address growing backlogs. Methods: First, we performed an international consultation through a four-stage consensus process to develop a multidomain index for hospital-level assessment (surgical preparedness index; SPI). Second, we measured surgical preparedness across a global network of hospitals in high-income countries (HICs), middle-income countries (MICs), and low-income countries (LICs) to explore the distribution of the SPI at national, subnational, and hospital levels. Finally, using COVID-19 as an example of an external system shock, we compared hospitals' SPI to their planned surgical volume ratio (SVR; ie, operations for which the decision for surgery was made before hospital admission), calculated as the ratio of the observed surgical volume over a 1-month assessment period between June 6 and Aug 5, 2021, against the expected surgical volume based on hospital administrative data from the same period in 2019 (ie, a pre-pandemic baseline). A linear mixed-effects regression model was used to determine the effect of increasing SPI score. Findings: In the first phase, from a longlist of 103 candidate indicators, 23 were prioritised as core indicators of elective surgical system preparedness by 69 clinicians (23 [33%] women; 46 [67%] men; 41 from HICs, 22 from MICs, and six from LICs) from 32 countries. The multidomain SPI included 11 indicators on facilities and consumables, two on staffing, two on prioritisation, and eight on systems. Hospitals were scored from 23 (least prepared) to 115 points (most prepared). In the second phase, surgical preparedness was measured in 1632 hospitals by 4714 clinicians from 119 countries. 745 (45·6%) of 1632 hospitals were in MICs or LICs. The mean SPI score was 84·5 (95% CI 84·1–84·9), which varied between HIC (88·5 [89·0–88·0]), MIC (81·8 [82·5–81·1]), and LIC (66·8 [64·9–68·7]) settings. In the third phase, 1217 (74·6%) hospitals did not maintain their expected SVR during the COVID-19 pandemic, of which 625 (51·4%) were from HIC, 538 (44·2%) from MIC, and 54 (4·4%) from LIC settings. In the mixed-effects model, a 10-point increase in SPI corresponded to a 3·6% (95% CI 3·0–4·1; p<0·0001) increase in SVR. This was consistent in HIC (4·8% [4·1–5·5]; p<0·0001), MIC (2·8 [2·0–3·7]; p<0·0001), and LIC (3·8 [1·3–6·7%]; p<0·0001) settings. Interpretation: The SPI contains 23 indicators that are globally applicable, relevant across different system stressors, vary at a subnational level, and are collectable by front-line teams. In the case study of COVID-19, a higher SPI was associated with an increased planned surgical volume ratio independent of country income status, COVID-19 burden, and hospital type. Hospitals should perform annual self-assessment of their surgical preparedness to identify areas that can be improved, create resilience in local surgical systems, and upscale capacity to address elective surgery backlogs. Funding: National Institute for Health Research (NIHR) Global Health Research Unit on Global Surgery, NIHR Academy, Association of Coloproctology of Great Britain and Ireland, Bowel Research UK, British Association of Surgical Oncology, British Gynaecological Cancer Society, and Medtronic

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Bi-allelic Loss-of-Function CACNA1B Mutations in Progressive Epilepsy-Dyskinesia.

The occurrence of non-epileptic hyperkinetic movements in the context of developmental epileptic encephalopathies is an increasingly recognized phenomenon. Identification of causative mutations provides an important insight into common pathogenic mechanisms that cause both seizures and abnormal motor control. We report bi-allelic loss-of-function CACNA1B variants in six children from three unrelated families whose affected members present with a complex and progressive neurological syndrome. All affected individuals presented with epileptic encephalopathy, severe neurodevelopmental delay (often with regression), and a hyperkinetic movement disorder. Additional neurological features included postnatal microcephaly and hypotonia. Five children died in childhood or adolescence (mean age of death: 9 years), mainly as a result of secondary respiratory complications. CACNA1B encodes the pore-forming subunit of the pre-synaptic neuronal voltage-gated calcium channel Cav2.2/N-type, crucial for SNARE-mediated neurotransmission, particularly in the early postnatal period. Bi-allelic loss-of-function variants in CACNA1B are predicted to cause disruption of Ca2+ influx, leading to impaired synaptic neurotransmission. The resultant effect on neuronal function is likely to be important in the development of involuntary movements and epilepsy. Overall, our findings provide further evidence for the key role of Cav2.2 in normal human neurodevelopment.MAK is funded by an NIHR Research Professorship and receives funding from the Wellcome Trust, Great Ormond Street Children's Hospital Charity, and Rosetrees Trust. E.M. received funding from the Rosetrees Trust (CD-A53) and Great Ormond Street Hospital Children's Charity. K.G. received funding from Temple Street Foundation. A.M. is funded by Great Ormond Street Hospital, the National Institute for Health Research (NIHR), and Biomedical Research Centre. F.L.R. and D.G. are funded by Cambridge Biomedical Research Centre. K.C. and A.S.J. are funded by NIHR Bioresource for Rare Diseases. The DDD Study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between the Wellcome Trust and the Department of Health, and the Wellcome Trust Sanger Institute (grant number WT098051). We acknowledge support from the UK Department of Health via the NIHR comprehensive Biomedical Research Centre award to Guy's and St. Thomas' National Health Service (NHS) Foundation Trust in partnership with King's College London. This research was also supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre. J.H.C. is in receipt of an NIHR Senior Investigator Award. The research team acknowledges the support of the NIHR through the Comprehensive Clinical Research Network. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, Department of Health, or Wellcome Trust. E.R.M. acknowledges support from NIHR Cambridge Biomedical Research Centre, an NIHR Senior Investigator Award, and the University of Cambridge has received salary support in respect of E.R.M. from the NHS in the East of England through the Clinical Academic Reserve. I.E.S. is supported by the National Health and Medical Research Council of Australia (Program Grant and Practitioner Fellowship)

MicroRNAs in the Cholangiopathies: Pathogenesis, Diagnosis, and Treatment

The cholangiopathies are a group of liver diseases resulting from different etiologies but with the cholangiocyte as the primary target. As a group, the cholangiopathies result in significant morbidity and mortality and represent one of the main indications for liver transplant in both children and adults. Contributing to this situation is the absence of a thorough understanding of their pathogenesis and a lack of adequate diagnostic and prognostic biomarkers. MicroRNAs are small non-coding RNAs that modify gene expression post-transcriptionally. They have been implicated in the pathogenesis of many diseases, including the cholangiopathies. Thus, in this review we provide an overview of the literature on miRNAs in the cholangiopathies and discuss future research directions

Initial and ongoing tobacco smoking elicits vascular damage and distinct inflammatory response linked to neurodegeneration

Tobacco smoking is strongly linked to vascular damage contributing to the development of hypertension, atherosclerosis, as well as increasing the risk for neurodegeneration. Still, the involvement of the innate immune system in the development of vascular damage upon chronic tobacco use before the onset of clinical symptoms is not fully characterized. Our data provide evidence that a single acute exposure to tobacco elicits the secretion of extracellular vesicles expressing CD105 and CD49e from endothelial cells, granting further recognition of early preclinical biomarkers of vascular damage. Furthermore, we investigated the effects of smoking on the immune system of healthy asymptomatic chronic smokers compared to never-smokers, focusing on the innate immune system. Our data reveal a distinct immune landscape representative for early stages of vascular damage in clinically asymptomatic chronic smokers, before tobacco smoking related diseases develop. These results indicate a dysregulated immuno-vascular axis in chronic tobacco smokers that are otherwise considered as healthy individuals. The distinct alterations are characterized by increased CD36 expression by the blood monocyte subsets, neutrophilia and increased plasma IL-18 and reduced levels of IL-33, IL-10 and IL-8. Additionally, reduced levels of circulating BDNF and elevated sTREM2, which are associated with neurodegeneration, suggest a considerable impact of tobacco smoking on CNS function in clinically healthy individuals. These findings provide profound insight into the initial and ongoing effects of tobacco smoking and the potential vascular damage contributing to neurodegenerative disorders, specifically cerebrovascular dysfunction and dementia

Landscape structure and farming management interacts to modulate pollination supply and crop production in blueberries

&lt;p&gt;&lt;span&gt;Pollination services are affected by landscape context, farming management, and pollinator community structure, all of which impact flower visitation rates, pollen deposition and final production. We studied these processes in Argentina for Highbush Blueberry crops which depend on pollinators to produce marketable yields. &lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;We studied how land cover and honeybee stocking influence the abundance of wild and managed pollinators in blueberry crops, using structural equation modeling to disentangle the cascading effects through which pollinators contribute to blueberry fruit number, size, nutritional content and overall yield. &lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;All pollinator functional groups responded to landscape changes at a spatial scale under 1000 m, and the significance or direction of the effects were modulated by the field-level deployment of honeybee hives. &lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;Fruit diameter increased with pollen deposited, but decreased with honeybee abundance, which, had indirect effects on fruit acidity and sugar content. Honeybees had a positive effect on the number of fruit produced by the plants and also benefited the overall yield (kg plant&lt;/span&gt;&lt;sup&gt;&lt;span&gt;-1&lt;/span&gt;&lt;/sup&gt;&lt;span&gt;) through independent effects on both the quality and quantity components of fruit production.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;&lt;em&gt;Synthesis and applications:&lt;/em&gt; &lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;Deployment of beehives in blueberry fields can buffer, but not compensate for the negative effects on honeybee abundance produced by surrounding large scale none-flowering crops. Such compensation would require high-quality beehives by monitoring their health and strength.&lt;/span&gt; &lt;span&gt;The &lt;/span&gt;&lt;span&gt;contribution of honeybees to crop production is not equal across production metrics. That is, higher abundance of honeybees increases the number of berries produced, but at the cost of smaller and more acidic fruits, potentially reducing market value. Growers must consider this trade-off between fruit quantity and quality when actively managing honeybee abundance. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;Funding provided by: Universidad Nacional de Tucumán&lt;br&gt;Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100007482&lt;br&gt;Award Number: PIUNT 2018 G609&lt;/p&gt;&lt;p&gt;Funding provided by: Natural Environment Research Council&lt;br&gt;Crossref Funder Registry ID: https://ror.org/02b5d8509&lt;br&gt;Award Number: SURPASS2 NE/S011870/2&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;span&gt;Sampling design and pollinator observation:&lt;/span&gt;&lt;span&gt; &lt;/span&gt;&lt;/em&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;In total &lt;/span&gt;&lt;span&gt;we sampled pollinators on nine farms, eight in 2020 and six in 2021 &lt;/span&gt;&lt;span&gt;—&lt;/span&gt;&lt;span&gt;five of these farms were sampled in both years. Five farms had honeybee hives to enhance crop pollination while four rely on wild pollinators or feral honeybees (Appendix 1 Figure S1a).&lt;/span&gt;&lt;span&gt; On each farm, we established an edge area (25 m from the field margin) and an inner area (50 m from the field margin), each comprising two 70 m transects running parallel to the field margin and separated by eight meters. On each of the four transects we selected 25 plants in full flower on which we conducted three 30-second pollinator counts, one in the morning, one at midday, and one in the afternoon. All sampling took place between 10:00 and 17:00 on sunny days with low wind and a temperature above 15 °C. We recorded the abundance of honeybees, small wild bees —any bee smaller than a honeybee — hoverflies (&lt;em&gt;Syrphidae&lt;/em&gt;), and butterflies. Hummingbirds and large wild bees (bumblebees&lt;/span&gt;&lt;span&gt; &lt;em&gt;Bombus&lt;/em&gt;&lt;/span&gt;&lt;span&gt; spp. and carpenter bees &lt;/span&gt;&lt;span&gt;&lt;em&gt;Xylocopa&lt;/em&gt; &lt;/span&gt;&lt;span&gt;spp., which are all larger than honeybees) showed behaviors that made them difficult to record as they fled when surveyors approached the plants. Therefore, after every 25 plant pollinator counts, we walked the same transect to record the abundance of hummingbirds and large wild bees for five minutes. This sampling protocol was repeated twice for each farm in each year (2020= 56 hours, 2021= 42 hours of pollinator counts).&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;em&gt;&lt;span&gt;Landscape classification:&lt;/span&gt;&lt;/em&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;All land use classifications were carried out using the interactive Google Earth Engine application 'Your Maps Your Way' (YMYW) (Morton &amp; &lt;/span&gt;&lt;span&gt;Schmucki, 2023&lt;/span&gt;&lt;span&gt;). A detailed explanation of the classification process can be found in Appendix 1, Table S1. In summary, we classified the different land covers into three broad categories: (i) natural cover (natural primary forest, secondary forest, and shrubland), (ii) flowering crops (citrus and blueberry), and (iii) large-scale crops (sugarcane and soybean crops). We grouped flowering crops together because blueberries occupy a small area compared to citrus, although both are perennial and provide comparable resources (Emerald bloom in August and citrus in September). We used the &lt;/span&gt;&lt;span&gt;&lt;em&gt;terra&lt;/em&gt; &lt;/span&gt;&lt;span&gt;(Hijmans 2022) and &lt;/span&gt;&lt;span&gt;&lt;em&gt;sf&lt;/em&gt; &lt;a href="https://paperpile.com/c/fyqtYO/M6RE"&gt;(&lt;em&gt;Pebesma&lt;/em&gt;, 2018)&lt;/a&gt;&lt;/span&gt;&lt;span&gt; R packages to define the area of the three land use classes within concentric circles with a radius of 200 to 4000 m around each site, progressively increasing the radius by 200 m (Appendix 1 Figure S1a). This was done to identify the spatial scale of pollinator responses to land use.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;em&gt;&lt;span&gt;Cascading effects: from pollinators to production &lt;/span&gt;&lt;/em&gt;&lt;/p&gt; &lt;p&gt;&lt;em&gt;&lt;span&gt;Pollinator counts:&lt;/span&gt;&lt;/em&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;During the 2021 flowering season (July – August), we used a subset of the nine farms (N = 3) to examine the effects of pollinators on blueberry pollen deposition and productivity metrics. On these farms growers manage pollination services using honeybee hives. On each farm, we selected five to six plots (Emerald cultivar, plot size 1.31 ± 0.09 ha) distributed from the edge of the farm to the interior to capture the potential variation in underlying pollination services, soil condition, irrigation system and plant age (Appendix 1 Figure S1b). The average distance between plots was 384.4 ± 197.5 m. In each plot we randomly selected five plants and conducted a 5-minute pollinator count per plant under the same weather conditions as mentioned above (26.6 ± 2.8 plants per farm). Previously, we estimated the floral display size per plant by combining the flowering percentage of the plant, the total fruits produced (see &lt;/span&gt;&lt;span&gt;&lt;em&gt;production metrics&lt;/em&gt; &lt;/span&gt;&lt;span&gt;section) and the fruit set of the Emerald cultivar at each farm (see the detailed procedure in Appendix 1 Table S3). We recorded&lt;/span&gt;&lt;span&gt;the abundance of honeybees and small wild pollinators. We also used the plant as a central point to record the abundance of hummingbirds and large wild bees in an area of 12 m&lt;/span&gt;&lt;sup&gt;&lt;span&gt;2&lt;/span&gt;&lt;/sup&gt;&lt;span&gt; (area occupied by 14 blueberry plants). The density of honeybee hives within 200 m (12.5 ha) of each sampled plot was recorded (Appendix 1 Figure S1b). Each plant was sampled twice during the ~30 day flowering period (13.3 hours of pollinator observation).&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;em&gt;&lt;span&gt;Pollen deposition:&lt;/span&gt;&lt;/em&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;After flower anthesis, we collected three styles per plant from senescent flowers (N&lt;/span&gt;&lt;span&gt;total&lt;/span&gt;&lt;span&gt; = 240). These were placed on a microscope slide after a transversal cut of the style at the stigma height and stained with Alexander's solution &lt;a href="https://paperpile.com/c/fyqtYO/1rrAY"&gt;(Alexander, 1969)&lt;/a&gt;. We then counted the number of pollen grains as a measure of the stigmatic pollen load.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;em&gt;&lt;span&gt;Production metrics:&lt;/span&gt;&lt;/em&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt; &lt;/span&gt;&lt;span&gt;We considered the following metrics to assess the quality and quantity of blueberry production: 1) We visited each sampled plant and counted the total number of fruits on two randomly selected primary branches. The product of the average number of fruits per primary branch and the number of primary branches was used as an estimate of the total number of fruits per plant; 2) We randomly selected ten mature fruits per sampled plant and measured their equatorial diameter; 3) Plant yield (kg plant&lt;/span&gt;&lt;sup&gt;&lt;span&gt;-1&lt;/span&gt;&lt;/sup&gt;&lt;span&gt;) was the product of average fruit weight (N&lt;/span&gt;&lt;span&gt;fruits per plant&lt;/span&gt;&lt;span&gt; = 10) and the total number of fruit produced; 4-7) Nutritional content based on a 30g fruit sample in the form of degrees Brix (measure of sugar content), total acidity, and concentration of anthocyanins (Appendix 1 in Table S4 for the laboratory protocol).&lt;/span&gt;&lt;/p&gt