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Species assemblages and their interactions vary through space, generating diversity patterns at different spatial scales. Here, we study the local‐scale spatial variation of a cavity‐nesting bee and wasp community (hosts), their nest associates (parasitoids), and the resulting antagonistic network over a continuous and homogeneous habitat. To obtain bee/wasp nests, we placed trap‐nests at 25 sites over a 32 km2 area. We obtained 1,541 nests (4,954 cells) belonging to 40 host species and containing 27 parasitoid species. The most abundant host species tended to have higher parasitism rate. Community composition dissimilarity was relatively high for both hosts and parasitoids, and the main component of this variability was species turnover, with a very minor contribution of ordered species loss (nestedness). That is, local species richness tended to be similar across the study area and community composition tended to differ between sites. Interestingly, the spatial matching between host and parasitoid composition was low. Host β‐diversity was weakly (positively) but significantly related to geographic distance. On the other hand, parasitoid and host‐parasitoid interaction β‐diversities were not significantly related to geographic distance. Interaction β‐diversity was even higher than host and parasitoid β‐diversity, and mostly due to species turnover. Interaction rewiring between plots and between local webs and the regional metaweb was very low. In sum, species composition was rather idiosyncratic to each site causing a relevant mismatch between hosts and parasitoid composition. However, pairs of host and parasitoid species tended to interact similarly wherever they co‐occurred. Our results additionally show that interaction β‐diversity is better explained by parasitoid than by host β‐diversity. We discuss the importance of identifying the sources of variation to understand the drivers of the observed heterogeneity., We demonstrate that communities of mobile organisms and their interactions vary at a local scale (~500 m) even in a continuous and homogeneous habitat. Parasitoid, rather than host, spatial turnover is the major driver of the observed spatial heterogeneity in host‐parasitoid interactions. Nonetheless, interactions are also subjected to their own intrinsic variability.

ObjectivesTo assess the degree of frailty in older people with different advanced diseases and its relationship with end-of-life illness trajectories and survival.MethodsProspective, observational study, including all patients admitted to the Acute Geriatric Unit of the University Hospital of Vic (Spain) during 12 consecutive months (2014–2015), followed for up to 2 years. Participants were identified as end-of-life people (EOLp) using the NECPAL (NECesidades PALiativas, palliative care needs) tool and were classified according to their dominant illness trajectory. The Frail-VIG index (Valoración Integral Geriátrica, Comprehensive Geriatric Assessment) was used to quantify frailty degree, to calculate the relationship between frailty and mortality (Receiver Operating Characteristic (ROC) curves), and to assess the combined effect of frailty degree and illness trajectories on survival (Cox proportional hazards model). Survival curves were plotted using the Kaplan-Meier estimator with participants classified into four groups (ie, no frailty, mild frailty, moderate frailty and advanced frailty) and were compared using the log-rank test.ResultsOf the 590 persons with a mean (SD) age of 86.4 (5.6) years recruited, 260 (44.1%) were identified as EOLp, distributed into cancer (n=31, 11.9%), organ failure (n=79, 30.4%), dementia (n=86, 33.1%) and multimorbidity (n=64, 24.6%) trajectories. All 260 EOLp had some degree of frailty, mostly advanced frailty (n=184, 70.8%), regardless of the illness trajectory, and 220 (84.6%) died within 2 years. The area under the ROC curve (95% CI) after 2 years of follow-up for EOLp was 0.87 (0.84 to 0.92) with different patterns of survival decline in the different end-of-life trajectories (pConclusionsAll older people towards the end-of-life in this study were frail, mostly with advanced frailty. The degree of frailty is related to survival across the different illness trajectories despite the differing survival patterns among trajectories. Frailty indexes may be useful to assess end-of-life older people, regardless of their trajectory.

Frailty; Psychometrics; Feasibility Fragilitat; Psicometria; Viabilitat Fragilidad; Psicometría; Viabilidad The study aimed to assess the reliability of the scores, evidence of validity, and feasibility of the Frail-VIG index. A validation study mixing hospitalized and community-dwelling older people was designed. Intraclass correlation coefficient (ICC) was used to assess the inter-rater agreement and the reliability. The construct validity of the Frail-VIG index with respect to the Frailty Phenotype (FP) was evaluated by calculating the area under the receiver operating characteristic curve (AUC-ROC). Convergent validity with the Clinical Frailty Scale (CFS) was assessed using Pearson’s correlation coefficients. The feasibility was evaluated by calculating the average time required to administer the Frail-VIG index and the percentage of unanswered responses. A sample of 527 older people (mean age of 81.61, 56.2% female) was included. The inter-rater agreement and test–retest reliability were very strong: 0.941 (95% CI, 0.890 to 0.969) and 0.976 (95% CI, 0.958 to 0.986), respectively. Results indicated adequate convergent validity of the Frail-VIG index with respect to the FP, AUC-ROC 0.704 (95% CI, 0.622 to 0.786), and a moderate to strong positive correlation between the Frail-VIG index and CFS (r = 0.635, 95% CI, 0.54 to 0.71). The Frail-VIG index administration required an average of 5.01 min, with only 0.34% of unanswered responses. The Frail-VIG index is a reliable, feasible, and valid instrument to assess the degree of frailty in hospitalized and community-dwelling older people.

International audience; Metals are naturally found in the environment but are also emitted through anthropogenic activities, raising some concerns about the potential deleterious effects of these elements on wildlife. The potential effects of metals on bird coloration have been the focus of several recent studies since animal colored-signals often reflect the physiology of their bearers and are thus used by animals to assess the quality of another individual as a mate or competitor. These studies have shown that the melanin pigmentation seems to be positively associated and the carotenoid-based coloration negatively associated with metal exposure in wild birds. Although these studies have been very useful to show the associations between metal exposure and coloration, only few of them have actually quantified the levels of metal exposure at the individual level; always focusing on one or two of them. Here, we measured the concentrations of eight metals in great tits' feathers and then assessed how these levels of metals were associated with the carotenoid and melanin-based colorations. We found that the melanin pigmentation was positively associated with the copper concentration and negatively correlated with the chromium concentration in feathers. In addition, we have shown that the carotenoid-based coloration was negatively associated with the feather's mercury concentration. This study is the first one to identify some metals that might affect positively and negatively the deposition of melanin and carotenoid into the plumage of wild birds.

Due to the contribution of honey bees (Apis mellifera) to wild flower and crop pollination, beekeeping has traditionally been considered a sustainable practice. However, high honey bee densities may have an impact on local pollen and nectar availability, which in turn may negatively affect other pollinators. This is exacerbated by the ability of honey bees to recruit foragers to highly rewarding flower patches. We measured floral resource consumption in rosemary (Rosmarinus officinalis) and thyme (Thymus vulgaris) in 21 plots located at different distances from apiaries in the scrubland of Garraf Natural Park (Barcelona), and related these measures to visitation rates of honey bees, bumblebees (Bombus terrestris) and other pollinators. In the same plots, we measured flower density, and used pan traps to characterize the wild bee community. Flower resource consumption was largely explained by honey bee visitation and marginally by bumblebee visitation. After accounting for flower density, plots close to apiaries had lower wild bee biomass. This was due to a lower abundance of large bee species, those more likely to be affected by honey bee competition. We conclude that honey bees are the main contributors to pollen/nectar consumption of the two main flowering plants in the scrubland, and that at the densities currently occurring in the park (3.5 hives/km2) the wild bee community is being affected. Our study supports the hypothesis that high honey bee densities may have an impact on other pollinators via competition for flower resources. Wegen des Beitrages der Honigbiene (Apis mellifera) bei der Bestäubung von Wildblumen und Nutzpflanzen wurde die Bienenhaltung traditionell als eine nachhaltige Aktivität angesehen. Indessen können hohe Honigbienendichten Auswirkungen auf die lokale Verfügbarkeit von Nektar und Pollen haben, was wiederum andere Bestäuber negativ beeinflussen könnte. Dies wird verstärkt durch die Fähigkeit der Honigbiene, Sammlerinnen zu lohnenden Sammelstellen zu dirigieren. Im Buschland des Garraf-Naturparks bei Barcelona maßen wir den Verbrauch von Blütenressourcen an Rosmarin (Rosmarinus officinalis) und Thymian (Thymus vulgaris) an 21 Standorten, die unterschiedlich weit von Bienenständen entfernt lagen, und setzten diese Werte in Bezug zu den Besuchsraten von Honigbienen, Hummeln (Bombus terrestris) und sonstigen Bestäubern. An den gleichen Standorten bestimmten wir die Blütendichte und setzten Farbschalen ein, um die Wildbienengemeinschaft zu erfassen. Die Nutzung der Blüt enressou wurde weitgehend durch die Besuchsraten der Honigbiene erklärt und in geringfügigem Maße durch Hummelbesuch. Nach Berücksichtigung der Blütendichte wiesen Standorte in der Nähe von Bienenständen eine geringere Wildbienen-Biomasse auf. Dies war auf eine geringere Abundanz der großen Wildbienenarten zurückzuführen, also der Arten, die wahrscheinlich durch die Konkurrenz der Honigbiene beeinträchtigt werden. Wir schließen, dass Honigbienen den größten Beitrag zum Pollen- bzw. Nektarverbrauch bei den beiden wichtigsten Blütenpflanzen des Gebietes leisten und dass die Wildbienengemeinschaft bei den gegenwärtigen Honigbienendichten im Park (3.5 Völker/km2) beeinflusst wird. Unsere Untersuchung unterstützt die Hypothese, dass hohe Honigbienendichten durch Konkurrenz um Blütenressourcen einen Einfluss auf andere Bestäuber haben könnten.

Understanding biodiversity distribution is a primary goal of community ecology. At a landscape scale, bee communities are affected by habitat composition, anthropogenic land use, and fragmentation. However, little information is available on local-scale spatial distribution of bee communities within habitats that are uniform at the landscape scale. We studied a bee community along with floral and nesting resources over a 32 km2 area of uninterrupted Mediterranean scrubland. Our objectives were (i) to analyze floral and nesting resource composition at the habitat scale. We ask whether these resources follow a geographical pattern across the scrubland at bee-foraging relevant distances; (ii) to analyze the distribution of bee composition across the scrubland. Bees being highly mobile organisms, we ask whether bee composition shows a homogeneous distribution or else varies spatially. If so, we ask whether this variation is irregular or follows a geographical pattern and whether bees respond primarily to flower or to nesting resources; and (iii) to establish whether body size influences the response to local resource availability and ultimately spatial distribution. We obtained 6580 specimens belonging to 98 species. Despite bee mobility and the absence of environmental barriers, our bee community shows a clear geographical pattern. This pattern is mostly attributable to heterogeneous distribution of small (

Introduction: In order to create value in health interventions, we should be able to assess the outcomes. M. Porter, in his article “What's value in health care”, proposes a new model for the assessment of health outcomes through the concept of value which refers to the output achieved relative to the cost incurred (VALUE = HEALTH RESULTS / COST). He proposes to assess health outcomes in three dimensions: 1. Health status achieved or maintained 2. Recovery Process 3. Sustainability of health Having a tool to assess daily clinical practice will allow us to identify the strengths and weaknesses of our interventions and generate further changes to improve them. Objective: To implement the evaluation results by the M. Porter method we selected a group of patients admitted to a geriatric medical-surgical unit. Patients were evaluated at three different times during the process (the moment of discharge, 1 month and 3 months later). Results: We have a sample of 100 patients, 25% were admitted for hip fracture and 75% for medical condition. The average age was 86.3 ± 4.1. The Barthel index (IB) before the admission was 81.15 ± 19.8 points. 2/3 of the patients admitted showed no cognitive impairment and 96% showed at least one geriatric syndrome prior to the admission. Except for 4 patients, all the remaining patients performed at least in one advanced activity of daily living. The average stay was 7.53 ± 3.8 days, during which a functional decline of 34.1 ± 25.52 points was detected (IB previous admission - IB discharge). The mortality rate during the 3-month follow-up was 19%. During the follow-up period we observed that the maximum functional recovery occurred in the first month (IB 1 month after discharge was 65.66 ± 27.4 points). Only 58.7% of patients return to their basal IB one month after discharge. At the baseline 69.7% of patients were able to walk outside their home, whereas only 41.9% can do it after 3 months of follow-up. Concerning hospitalization, in 82.5% of patients some adverse effects were observed, confusional syndrome being the most common (36.2%). Intermediate care resources were used by 71% of the patients surveyed due to the functional decline at discharge. Referring to clinical stability, 34% of patients have a further crisis event that requires contact with the health system. This new contact takes place after 32.23 ± 38.9 days on average. 26 patients presented two or more crisis events during the follow-up period. Primary care was the only resource used in the follow up of 18% of the patients. In the 3-month follow-up survey we asked a group of questions about the experience of the patient and/or their family during the attention received by the health care system during the whole process: 73.2% of them reported having been attended in the appropriate time-frame, 90% reported positively about the cause of consultation being solved and being informed about their health problems and tests results. The average of affirmative answers to the question of whether they had been involved in making decisions during the illness process was 61.1%. Conclusions: These results allow us to affirm that hospitalization for an acute problem marks a turning point in the health of patients. The most relevant result is cognitive and functional decline that occurs in over 50% of cases. In addition the functional loss has a big impact on mortality and use of resources because this population has a low clinical stability. This group of indicators evaluates results of health systems, not just the acute care hospital ones, therefore allows us to get a picture of the overall performance of the health system. This leads us to think that the best response of the health system for the care of aged, frailty and multimorbidity patients is clinical integration. In order to improve our interventions and give them a greater value, we need to act at different levels: Detection of frail patients at risk prior to a crisis event, use of early identification systems to allow home hospitalization, enhance the activity of rapid action teams that act on special groups prone to present frequent crisis events such as institutionalized patients and improve the work carried out by hospital at home teams.

Introduction: Drug therapy in older patients is becoming a worldwide concern due to the progressive increase in the number of drug prescriptions and the difficulties in ensuring appropriate prescribing. Appropriate prescribing in these patients is a complex problem that cannot be solved by simply applying clinical practice guidelines (CPG). Patients with multimorbidity usually show an evolution towards a progressive clinical, functional or cognitive impairment, which, in most cases, results in a limited life prognosis. In this scenario, drug therapy, which could have been appropriate in the past, can eventually become inappropriate as a disease progresses. Frail older patients are more sensitive to inappropriate prescribing (IP), as they suffer a reduction in their ability to tolerate medications due to changes in pharmacokinetics and pharmacodynamics. Consensus on the best procedure to optimize prescribing has not yet been established. In this context, we propose the use of a model which combines both the clinical judgment and the scientific evidence in a pragmatic and systematic approach. The main objective of this model is to identify potentially IP in a group of older patients and to optimize them according to care goals of each patient. Care goals are established through the application of the Patient-Centered Prescription Model, which is based on a shared decision-making process including patient, physicians and a clinical pharmacist. Description of practice: Each patient’s pharmacotherapeutic plan is assessed through application of the Patient-Centered Prescription Model[1,2]. In brief, this is a systematic three-step process carried out by a multidisciplinary team made up of geriatricians and a clinical pharmacist: Step 1 - Patient Centered Evaluation The main objective of this step is to determine the global care goal of each patient: survival, improving or maintaining function or symptomatic control[3]. A holistic review of the patient, by means of a Comprehensive geriatric assessment, determines his care goal. Professionals and patients establish a cooperative process to identify needs and agree on objectives[4]. This first evaluation sets the stage for the second and third steps. Step 2 - Diagnosis Centered Evaluation: Health problems of the patient along with the drugs prescribed for each diagnosis are listed. Drug purpose should fit with the main care goal previously agreed upon with the patient. Special attention is paid to prevalent disease states (Diabetes Mellitus (DM2)[5], arterial hypertension[6], dyslipidemia[7], Pain[8][9] and end of life situation[3,10]). Step 3 - Medication centered assessment: Based on the goals of care derived from step 1, the purpose of step 3 is to assess the indication of high risk medications (NSAID, antiplatelets, anticoagulants, hypoglycemics, insulin, digoxin and opioids[11]) or high risk combinations (risk of drug-drug or drug-disease interactions)[12] Proposed therapeutic plan: At the end of the process, as an integrated care approach, an individualized treatment plan is sent (or discussed by phone) for review and agreement with primary care. Key findings: These are the results of three descriptive observational studies of patients admitted to an Acute Care Elderly (ACE) Unit, in three periods from 2012 to 2015. 382 patients were included. Average age of 86.7 years. 40% of all patients met the criteria for advanced chronic conditions (ACC). At admission, median of medications per patient was 7, no differences between patients with ACC and the rest. 80% of patients met the definition for polypharmacy, no significance with patients with ACC. 33% of patients with ACC took 10 or more medications. 20% of total of patients had 2 or more iatrogenic medications prescribed, no differences between patients with ACC and the rest. Overall, almost 40% of patients presented with an indication of at least one IP. Patients with ACC were significantly more likely to have IP (47.2% versus 34.7%) (p During admission, drug therapy regimens were modified in 93.44% of cases with IP. Prevalence of polypharmacy decreased significantly and, the prevalence of patients with ACC with 10 or more medications decreased to a half (p Analizing patients with advanced dementia, 24.80% of their medications were for prevention, of which 76.2% were for primary prevention. At discharge, two-thirds (66.85%) of them were discontinued. Highlights: The proposed methodology represents an initial approach encompassing evidence from multiple fields on the appropriateness of prescribing in the elderly and facilitating application of evidence in the routine clinical setting. Patient is the center of attention and he is an active member in the decision-making process. Conclusions: Patients with advanced chronic conditions do not have a prescription according to their needs. The Patient-Centered Prescription Model is a framework that helps minimize IP in a high-risk group older patients through a suitable approach to individualize a pharmacotherapeutic plan. It is applicable in other healthcare settings, such as nursing-homes or community care. References: 1. Molist Brunet N, Sevilla-Sanchez D, Amblas Novellas J, Codina Jane C, Gomez-Batiste X, McIntosh J, et al. Optimizing drug therapy in patients with advanced dementia: A patient-centered approach. Eur. Geriatr. Med. 2014;5:66–71. 2. Molist Brunet N, Espaulella Panicot J, Sevilla Sanchez D, Amblas Novellas J, Codina Jane C, Jacint A, et al. A patient-centered prescription model assessing the appropriateness of chronic drug therapy in older patients at the end of life. Eur. Geriatr. Med. 2015;3–7. 3. O’Mahony D, O’Connor MN. Pharmacotherapy at the end-of-life. Age Ageing. 2011;40:419–22. 4. Coulter A, Collins A (2011). Making shared decision-Making a reality. No decision about me, without me. The Kings Fund. (acc. 15 Apr 2015) Available at: http://www.kingsfund.org.uk/sites/files/kf/Making-shared-decision-making-a-reality-paper-Angela-Coulter-Alf-Collins-July-2011_0.pdf 5. Sue Kirkman M, Briscoe VJ, Clark N, Florez H, Haas L, Halter J, et al. Diabetes in older adults: a consensus report. J. Am. Geriatr. Soc. 2012;60:2342–56. 6. Goeres LM, Williams CD, Eckstrom E, Lee DSH. Pharmacotherapy for hypertension in older adults: a systematic review. Drugs Aging. 2014;31:897–910. 7. Petersen LK, Christensen K, Kragstrup J. Lipid-lowering treatment to the end? A review of observational studies and RCTs on cholesterol and mortality in 80+-year olds. Age Ageing. 2010;39:674–80. 8. Abdulla A, Adams N, Bone M et al. Guidance on the management of pain in older people. Age Ageing. 2013;42 Suppl 1:1–57. 9. Articles S. American Geriatrics Society updated Beers Criteria for potentially inappropriate medication use in older adults. J. Am. Geriatr. Soc. 2012;60:616–31. 10. Cruz-Jentoft AJ, Boland B, Rexach L. Drug therapy optimization at the end of life. Drugs and Aging. 2012;29(8):511–21. 11. Budnitz DS, Lovegrove MC, Shehab N, Richards CL et al. Emergency hospitalizations for adverse drug events in older Americans. N. Engl. J. Med. 2011;365:2002–12. 12. The Model of Care PolypharmacyWorking Group. Polypharmacy Guidance 2012. NHS Scotland. 2013.