Elevational homogenization of mountain parasitoids across six decades.

Elevational gradients are characterized by strong environmental changes within small geographical distances, providing important insights on the response of biological communities to climate change. Mountain biodiversity is particularly sensitive to climate change, given the limited capacity to colonize new areas and the competition from upshifting lowland species. Knowledge on the impact of climate change on mountain insect communities is patchy, but elevation is known to influence parasitic interactions which control insect communities and functions within ecosystems. We analyzed a European dataset of bristle flies, a parasitoid group which regulates insect herbivory in both managed and natural ecosystems. Our dataset spans six decades and multiple elevational bands, and we found marked elevational homogenization in the host specialization of bristle fly species through time. The proportion of specialized parasitoids has increased by ca. 70% at low elevations, from 17 to 29%, and has decreased by ca. 20% at high elevations, from 48 to 37%. As a result, the strong elevational gradient in bristle fly specialization observed in the 1960s has become much flatter over time. As climate warming is predicted to accelerate, the disappearance of specialized parasitoids from high elevations might become even faster. This parasitoid homogenization can reshape the ecological function of mountain insect communities, increasing the risk of herbivory outbreak at high elevations. Our results add to the mounting evidence that symbiotic species might be especially at risk from climate change: Monitoring the effects of these changes is urgently needed to define effective conservation strategies for mountain biodiversity.

Modelling the probability of meeting IUCN Red List criteria to support reassessments.

Comparative extinction risk analysis-which predicts species extinction risk from correlation with traits or geographical characteristics-has gained research attention as a promising tool to support extinction risk assessment in the IUCN Red List of Threatened Species. However, its uptake has been very limited so far, possibly because existing models only predict a species' Red List category, without indicating which Red List criteria may be triggered. This prevents such approaches to be integrated into Red List assessments. We overcome this implementation gap by developing models that predict the probability of species meeting individual Red List criteria. Using data on the world's birds, we evaluated the predictive performance of our criterion-specific models and compared it with the typical criterion-blind modelling approach. We compiled data on biological traits (e.g. range size, clutch size) and external drivers (e.g. change in canopy cover) often associated with extinction risk. For each specific criterion, we modelled the relationship between extinction risk predictors and species' Red List category under that criterion using ordinal regression models. We found criterion-specific models were better at identifying threatened species compared to a criterion-blind model (higher sensitivity), but less good at identifying not threatened species (lower specificity). As expected, different covariates were important for predicting extinction risk under different criteria. Change in annual temperature was important for criteria related to population trends, while high forest dependency was important for criteria related to restricted area of occupancy or small population size. Our criteria-specific method can support Red List assessors by producing outputs that identify species likely to meet specific criteria, and which are the most important predictors. These species can then be prioritised for re-evaluation. We expect this new approach to increase the uptake of extinction risk models in Red List assessments, bridging a long-standing research-implementation gap.

Using comparative extinction risk analysis to prioritize the IUCN Red List reassessments of amphibians.

Assessing the extinction risk of species based on the International Union for Conservation of Nature (IUCN) Red List (RL) is key to guiding conservation policies and reducing biodiversity loss. This process is resource demanding, however, and requires continuous updating, which becomes increasingly difficult as new species are added to the RL. Automatic methods, such as comparative analyses used to predict species RL category, can be an efficient alternative to keep assessments up to date. Using amphibians as a study group, we predicted which species are more likely to change their RL category and thus should be prioritized for reassessment. We used species biological traits, environmental variables, and proxies of climate and land-use change as predictors of RL category. We produced an ensemble prediction of IUCN RL category for each species by combining 4 different model algorithms: cumulative link models, phylogenetic generalized least squares, random forests, and neural networks. By comparing RL categories with the ensemble prediction and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessment based on the mismatch between predicted and observed values. The most important predicting variables across models were species' range size and spatial configuration of the range, biological traits, climate change, and land-use change. We compared our proposed prioritization index and the predicted RL changes with independent IUCN RL reassessments and found high performance of both the prioritization and the predicted directionality of changes in RL categories. Ensemble modeling of RL category is a promising tool for prioritizing species for reassessment while accounting for models' uncertainty. This approach is broadly applicable to all taxa on the IUCN RL and to regional and national assessments and may improve allocation of the limited human and economic resources available to maintain an up-to-date IUCN RL.

Uso del análisis comparativo del riesgo de extinción para priorizar la reevaluación de los anfibios en la Lista Roja de la UICN Resumen El análisis del riesgo de extinción de una especie con base en la Lista Roja (LR) de la Unión Internacional para la Conservación de la Naturaleza (UICN) es clave para guiar las políticas de conservación y reducir la pérdida de la biodiversidad. Sin embargo, este proceso demanda recursos y requiere de actualizaciones continuas, lo que se complica conforme se añaden especies nuevas a la LR. Los métodos automáticos, como los análisis comparativos usados para predecir la categoría de la especie en la LR, pueden ser una alternativa eficiente para mantener actualizados los análisis. Usamos a los anfibios como grupo de estudio para predecir cuáles especies tienen mayor probabilidad de cambiar de categoría en la LR y que, por lo tanto, se debería priorizar su reevaluación. Usamos las características biológicas de la especie, las variables ambientales e indicadores climáticos y del cambio de uso de suelo como predictores de la categoría en la LR. Elaboramos una predicción de ensamble de la categoría en la LR de la UICN para cada especie mediante la combinación de cuatro algoritmos diferentes: modelos de vínculo acumulativo, menor número de cuadros filogenéticos generalizados, bosques aleatorios y redes neurales. Con la comparación entre las categorías de la LR y la predicción de ensamble y con considerar la incertidumbre entre los algoritmos identificamos especies que deberían ser prioridad para futuras reevaluaciones con base en el desfase entre los valores predichos y los observados. Las variables de predicción más importantes entre los modelos fueron el tamaño de la distribución de la especie y su configuración espacial, las características biológicas, el cambio climático y el cambio de uso de suelo. Comparamos nuestra propuesta de índice de priorización y los cambios predichos en la LR con las reevaluaciones independientes de la LR de la UICN y descubrimos un buen desempeño tanto para la priorización como para la direccionalidad predicha de los cambios en las categorías de la LR. El modelo de ensamble de la categoría de la LR esa una herramienta prometedora para priorizar la reevaluación de las especies a la vez que considera la incertidumbre del modelo. Esta estrategia puede generalizarse para aplicarse a todos los taxones de la LR de la UICN y a los análisis regionales y nacionales. También podría mejorar la asignación de los recursos humanos y económicos limitados disponibles para mantener actualizada la LR de la UICN.

A standard approach for including climate change responses in IUCN Red List assessments.

The International Union for Conservation of Nature (IUCN) Red List is a central tool for extinction risk monitoring and influences global biodiversity policy and action. But, to be effective, it is crucial that it consistently accounts for each driver of extinction. Climate change is rapidly becoming a key extinction driver, but consideration of climate change information remains challenging for the IUCN. Several methods can be used to predict species' future decline, but they often fail to provide estimates of the symptoms of endangerment used by IUCN. We devised a standardized method to measure climate change impact in terms of change in habitat quality to inform criterion A3 on future population reduction. Using terrestrial nonvolant tetrapods as a case study, we measured this impact as the difference between the current and the future species climatic niche, defined based on current and future bioclimatic variables under alternative model algorithms, dispersal scenarios, emission scenarios, and climate models. Our models identified 171 species (13% out of those analyzed) for which their current red-list category could worsen under criterion A3 if they cannot disperse beyond their current range in the future. Categories for 14 species (1.5%) could worsen if maximum dispersal is possible. Although ours is a simulation exercise and not a formal red-list assessment, our results suggest that considering climate change impacts may reduce misclassification and strengthen consistency and comprehensiveness of IUCN Red List assessments.

Una estrategia estándar para incluir las respuestas al cambio climático en las evaluaciones de la Lista Roja de la UICN Resumen La Lista Roja de la Unión Internacional para la Conservación de la Naturaleza (UICN) es una herramienta central para el monitoreo del riesgo de extinción e influye sobre las acciones y políticas para la biodiversidad. Para que esta herramienta sea efectiva, es crucial que tenga en cuenta de manera regular cada factor de extinción. El cambio climático se está convirtiendo rápidamente en un factor de extinción importante, pero considerar información sobre este factor todavía es un reto para la UICN. Se pueden usar varios métodos para predecir la declinación de una especie en el futuro, pero generalmente fallan en proporcionar estimaciones de los síntomas del peligro usados por la UICN. Diseñamos un método estandarizado para medir el impacto del cambio climático en términos del cambio en la calidad del hábitat para informar el criterio A3 sobre la reducción futura de las poblaciones. Usamos a los tetrápodos terrestres no voladores como estudio de caso para medir este impacto como la diferencia entre el nicho climático actual y futuro de las especies, definido con base en las variables bioclimáticas actuales y futuras con algoritmos de modelos alternativos, escenarios de dispersión y emisión y modelos climáticos. Nuestros modelos identificaron 171 especies (13% de las especies analizadas) para las que su categoría actual en la lista roja podría empeorar bajo el criterio A3 si no logran dispersarse más allá de su distribución actual en el futuro. Las categorías para 14 especies (1.5%) podrían empeorar si es posible la dispersión máxima. Aunque realizamos una simulación y no una evaluación formal para listas rojas, nuestros resultados sugieren que considerar los impactos del cambio climático podría reducir la clasificación incorrecta y fortalecer la coherencia y exhaustividad de las evaluaciones de la Lista Roja de la UICN.

Implantable defibrillator-detected heart failure status predicts ventricular tachyarrhythmias.

INTRODUCTION: The prediction of ventricular tachyarrhythmias among patients with implantable cardioverter defibrillators is difficult with available clinical tools. We sought to assess whether in patients with heart failure (HF) and reduced ejection fraction with defibrillators, physiological sensor-based HF status, as summarized by the HeartLogic index, could predict appropriate device therapies. METHODS: Five hundred and sixty-eight consecutive HF patients with defibrillators (n = 158, 28%) or cardiac resynchronization therapy-defibrillators (n = 410, 72%) were included in this prospective observational multicenter analysis. The association of both HeartLogic index and its physiological components with defibrillator shocks and overall appropriate therapies was assessed in regression and time-dependent Cox models. RESULTS: Over a follow-up of 25 (15-35) months, 122 (21%) patients received an appropriate device therapy (shock, n = 74, 13%), while the HeartLogic index crossed the threshold value (alert, HeartLogic ≥ 16) 1200 times (0.71 alerts/patient-year) in 370 (65%) subjects. The occurrence of ≥1 HeartLogic alert was significantly associated with both appropriate shocks (Hazard ratios [HR]: 2.44, 95% confidence interval [CI]: 1.49-3.97, p = .003), and any appropriate defibrillator therapies. In multivariable time-dependent Cox models, weekly IN-alert state was the strongest predictor of appropriate defibrillator shocks (HR: 2.94, 95% CI: 1.73-5.01, p < .001) and overall therapies. Compared with stable patients, patients with appropriate shocks had significantly higher values of HeartLogic index, third heart sound amplitude, and resting heart rate 30-60 days before device therapy. CONCLUSION: The HeartLogic index is an independent dynamic predictor of appropriate defibrillator therapies. The combined index and its individual physiological components change before the arrhythmic event occurs.

Prioritizing the reassessment of data-deficient species on the IUCN Red List.

Despite being central to the implementation of conservation policies, the usefulness of the International Union for Conservation of Nature (IUCN) Red List of Threatened Species is hampered by the 14% of species classified as data-deficient (DD) because information to evaluate these species' extinction risk was lacking when they were last assessed or because assessors did not appropriately account for uncertainty. Robust methods are needed to identify which DD species are more likely to be reclassified in one of the data-sufficient IUCN Red List categories. We devised a reproducible method to help red-list assessors prioritize reassessment of DD species and tested it with 6887 DD species of mammals, reptiles, amphibians, fishes, and Odonata (dragonflies and damselflies). For each DD species in these groups, we calculated its probability of being classified in a data-sufficient category if reassessed today from covariates measuring available knowledge (e.g., number of occurrence records or published articles available), knowledge proxies (e.g., remoteness of the range), and species characteristics (e.g., nocturnality); calculated change in such probability since last assessment from the increase in available knowledge (e.g., new occurrence records); and determined whether the species might qualify as threatened based on recent rate of habitat loss determined from global land-cover maps. We identified 1907 species with a probability of being reassessed in a data-sufficient category of >0.5; 624 species for which this probability increased by >0.25 since last assessment; and 77 species that could be reassessed as near threatened or threatened based on habitat loss. Combining these 3 elements, our results provided a list of species likely to be data-sufficient such that the comprehensiveness and representativeness of the IUCN Red List can be improved.

Priorización de la reevaluación de las especies con datos deficientes en la Lista Roja de la UICN Resumen No obstante que es fundamental para la implementación de políticas de conservación, la utilidad de la Lista Roja de Especies Amenazadas de la Unión Internacional para la Conservación de la Naturaleza (UICN) está limitada por el 14% de especies clasificadas con datos deficientes (DD) debido a que la información para evaluar el riesgo de extinción de estas especies no existía cuando fueron evaluadas la última vez o porque los evaluadores no consideraron la incertidumbre apropiadamente. Se requieren métodos robustos para identificar las especies DD con mayor probabilidad de ser reclasificadas en alguna de las categorías en la Lista Roja UICN con datos suficientes. Diseñamos un método reproducible para ayudar a que los evaluadores de la lista roja prioricen la reevaluación de especies DD y lo probamos con 6,887 especies DD de mamíferos, reptiles, anfibios, peces y Odonata (libélulas y caballitos del diablo). Para cada una de las especies DD en estos grupos, calculamos la probabilidad de ser clasificadas en una categoría con datos suficientes si fuera reevaluada hoy a partir de covariables que miden el conocimiento disponible (e.g., número de registros de ocurrencia o artículos publicados disponibles), sustitutos de conocimiento (e.g., extensión del rango de distribución) y características de la especie ((e.g., nocturnidad); calculamos el cambio en tal probabilidad desde la última reevaluación a partir del incremento en el conocimiento disponible (e.g., registros de ocurrencia nuevos); y determinamos si las especies podrían calificar como amenazadas con base en pérdidas de hábitat recientes a partir de mapas globales de cobertura de suelo recientes. Identificamos 1,907 especies con una probabilidad >0.5 de ser reclasificados en una categoría con datos suficientes; 624 especies cuya probabilidad aumentó en >0.25 desde la última evaluación, y 77 especies que podrían ser reclasificadas como casi en peligro con base en la pérdida de hábitat. Combinando estos 3 elementos, nuestros resultados proporcionaron una lista de especies probablemente con datos suficientes de tal modo que la exhaustividad y la representatividad de la Lista Roja de la UICN pueden ser mejoradas.

Combination of an implantable defibrillator multi-sensor heart failure index and an apnea index for the prediction of atrial high-rate events.

AIMS: Patients with atrial fibrillation frequently experience sleep disorder breathing, and both conditions are highly prevalent in presence of heart failure (HF). We explored the association between the combination of an HF and a sleep apnoea (SA) index and the incidence of atrial high-rate events (AHRE) in patients with implantable defibrillators (ICDs). METHODS AND RESULTS: Data were prospectively collected from 411 consecutive HF patients with ICD. The IN-alert HF state was measured by the multi-sensor HeartLogic Index (>16), and the ICD-measured Respiratory Disturbance Index (RDI) was computed to identify severe SA. The endpoints were as follows: daily AHRE burden of ≥5 min, ≥6 h, and ≥23 h. During a median follow-up of 26 months, the time IN-alert HF state was 13% of the total observation period. The RDI value was ≥30 episodes/h (severe SA) during 58% of the observation period. An AHRE burden of ≥5 min/day was documented in 139 (34%) patients, ≥6 h/day in 89 (22%) patients, and ≥23 h/day in 68 (17%) patients. The IN-alert HF state was independently associated with AHRE regardless of the daily burden threshold: hazard ratios from 2.17 for ≥5 min/day to 3.43 for ≥23 h/day (P < 0.01). An RDI ≥ 30 episodes/h was associated only with AHRE burden ≥5 min/day [hazard ratio 1.55 (95% confidence interval: 1.11-2.16), P = 0.001]. The combination of IN-alert HF state and RDI ≥ 30 episodes/h accounted for only 6% of the follow-up period and was associated with high rates of AHRE occurrence (from 28 events/100 patient-years for AHRE burden ≥5 min/day to 22 events/100 patient-years for AHRE burden ≥23 h/day). CONCLUSIONS: In HF patients, the occurrence of AHRE is independently associated with the ICD-measured IN-alert HF state and RDI ≥ 30 episodes/h. The coexistence of these two conditions occurs rarely but is associated with a very high rate of AHRE occurrence. CLINICAL TRIAL REGISTRATION: URL: http://clinicaltrials.gov/Identifier: NCT02275637.

Performance of a multi-sensor implantable defibrillator algorithm for heart failure monitoring in the presence of atrial fibrillation.

AIMS: The HeartLogic Index combines data from multiple implantable cardioverter defibrillators (ICDs) sensors and has been shown to accurately stratify patients at risk of heart failure (HF) events. We evaluated and compared the performance of this algorithm during sinus rhythm and during long-lasting atrial fibrillation (AF). METHODS AND RESULTS: HeartLogic was activated in 568 ICD patients from 26 centres. We found periods of ≥30 consecutive days with an atrial high-rate episode (AHRE) burden <1 h/day and periods with an AHRE burden ≥20 h/day. We then identified patients who met both criteria during the follow-up (AHRE group, n = 53), to allow pairwise comparison of periods. For control purposes, we identified patients with an AHRE burden <1 h throughout their follow-up and implemented 2:1 propensity score matching vs. the AHRE group (matched non-AHRE group, n = 106). In the AHRE group, the rate of alerts was 1.2 [95% confidence interval (CI): 1.0-1.5]/patient-year during periods with an AHRE burden <1 h/day and 2.0 (95% CI: 1.5-2.6)/patient-year during periods with an AHRE-burden ≥20 h/day (P = 0.004). The rate of HF hospitalizations was 0.34 (95% CI: 0.15-0.69)/patient-year during IN-alert periods and 0.06 (95% CI: 0.02-0.14)/patient-year during OUT-of-alert periods (P < 0.001). The IN/OUT-of-alert state incidence rate ratio of HF hospitalizations was 8.59 (95% CI: 1.67-55.31) during periods with an AHRE burden <1 h/day and 2.70 (95% CI: 1.01-28.33) during periods with an AHRE burden ≥20 h/day. In the matched non-AHRE group, the rate of HF hospitalizations was 0.29 (95% CI: 0.12-0.60)/patient-year during IN-alert periods and 0.04 (95% CI: 0.02-0.08)/patient-year during OUT-of-alert periods (P < 0.001). The incidence rate ratio was 7.11 (95% CI: 2.19-22.44). CONCLUSION: Patients received more alerts during periods of AF. The ability of the algorithm to identify increased risk of HF events was confirmed during AF, despite a lower IN/OUT-of-alert incidence rate ratio in comparison with non-AF periods and non-AF patients. CLINICAL TRIAL REGISTRATION: http://clinicaltrials.gov/Identifier: NCT02275637.

HeartLogic™: real-world data-efficiency, resource consumption, and workflow optimization.

Heart failure (HF) is a major and still growing medical problem and is characterized by episodes of acute decompensation that are associated with a negative prognosis and a significant burden on the patients, doctors, and healthcare resources. Early detection of incipient HF may allow outpatient treatment before patients severely decompensate, thus reducing HF hospitalizations and related costs. The HeartLogic™ algorithm is an automatic, remotely managed system combining data directly related to HF pathophysiology into a single score, the HeartLogic™ index. This index proved to be effective in predicting the risk of incipient HF decompensation, allowing to redistribute resources from low-risk to high-risk patients in a timely and cost-saving manner. The alert-based remote management system seems more efficient than the one based on scheduled remote transmission in terms of caregivers' workload and alert detection timing. The widespread application of the HeartLogic™ algorithm requires the resolution of logistical and financial issues and the adoption of a pre-defined, functional workflow. In this paper, we reviewed general aspects of remote monitoring in HF patients, the functioning and pathophysiological basis of the HeartLogic index, its efficiency in the management of HF patients, and the economic effects and the organizational revolution associated with its use.

Global impacts of climate change on avian functional diversity.

Climate change is predicted to drive geographical range shifts, leading to fluctuations in species richness (SR) worldwide. However, the effect of these changes on functional diversity (FD) remains unclear, in part because comprehensive species-level trait data are generally lacking at global scales. Here, we use morphometric and ecological traits for 8268 bird species to estimate the impact of climate change on avian FD. We show that future bird assemblages are likely to undergo substantial shifts in trait structure, with a magnitude of change greater than predicted from SR alone, and a direction of change varying according to geographical location and trophic guild. For example, our models predict that FD of insect predators will increase at higher latitudes with concurrent losses at mid-latitudes, whereas FD of seed dispersing birds will fluctuate across the tropics. Our findings highlight the potential for climate change to drive continental-scale shifts in avian FD with implications for ecosystem function and resilience.

Patient acceptance of subcutaneous versus transvenous defibrillator systems: A multi-center experience.

BACKGROUND: The subcutaneous implantable cardioverter-defibrillator (S-ICD) is an effective alternative to the transvenous ICD. No study has yet compared S-ICD and transvenous ICD by assessing patient acceptance as a patient-centered outcome. OBJECTIVE: To evaluate the patient acceptance of the S-ICD and to investigate its association with clinical and implantation variables. In patients with symptomatic heart failure and reduced ejection fraction (HFrEF), the acceptance of the S-ICD was compared with a control group of patients who received a transvenous ICD. METHODS: Patient acceptance was calculated with the Florida Patient Acceptance Survey (FPAS) which measures four factors: return to function (RTF), device-related distress (DRD), positive appraisal (PA), and body image concerns (BIC). The survey was administered 12 months after implantation. RESULTS: 176 patients underwent S-ICD implantation. The total FPAS and the single factors did not differ according to gender, body habitus, or generator positioning. Patients with HFrEF had lower FPAS and RTF. Younger patients showed better RTF (75 [56-94] vs. 56 [50-81], p = .029). Patients who experienced device complications or device therapies showed higher DRD (40 [35-60] vs. 25 [10-50], p = .019). Patients with HFrEF receiving the S-ICD had comparable FPAS, RTF, DRD, and BIC to HFrEF patients implanted with the transvenous ICD while exhibited significantly better PA (88 [75-100] vs. 81 [63-94], p = .02). CONCLUSIONS: Our analysis revealed positive patient acceptance of the S-ICD, even in groups at risk of more distress such as women or patients with thinner body habitus, and regardless of the generator positioning. Among patients receiving ICDs for HFrEF, S-ICD was associated with better PA versus transvenous ICD.

Intact but empty forests? Patterns of hunting-induced mammal defaunation in the tropics.

Tropical forests are increasingly degraded by industrial logging, urbanization, agriculture, and infrastructure, with only 20% of the remaining area considered intact. However, this figure does not include other, more cryptic but pervasive forms of degradation, such as overhunting. Here, we quantified and mapped the spatial patterns of mammal defaunation in the tropics using a database of 3,281 mammal abundance declines from local hunting studies. We simultaneously accounted for population abundance declines and the probability of local extirpation of a population as a function of several predictors related to human accessibility to remote areas and species' vulnerability to hunting. We estimated an average abundance decline of 13% across all tropical mammal species, with medium-sized species being reduced by >27% and large mammals by >40%. Mammal populations are predicted to be partially defaunated (i.e., declines of 10%-100%) in ca. 50% of the pantropical forest area (14 million km2), with large declines (>70%) in West Africa. According to our projections, 52% of the intact forests (IFs) and 62% of the wilderness areas (WAs) are partially devoid of large mammals, and hunting may affect mammal populations in 20% of protected areas (PAs) in the tropics, particularly in West and Central Africa and Southeast Asia. The pervasive effects of overhunting on tropical mammal populations may have profound ramifications for ecosystem functioning and the livelihoods of wild-meat-dependent communities, and underscore that forest coverage alone is not necessarily indicative of ecosystem intactness. We call for a systematic consideration of hunting effects in (large-scale) biodiversity assessments for more representative estimates of human-induced biodiversity loss.

Time-lagged effects of habitat fragmentation on terrestrial mammals in Madagascar.

Biodiversity is severely threatened by habitat destruction. As a consequence of habitat destruction, the remaining habitat becomes more fragmented. This results in time-lagged population extirpations in remaining fragments when these are too small to support populations in the long term. If these time-lagged effects are ignored, the long-term impacts of habitat loss and fragmentation will be underestimated. We quantified the magnitude of time-lagged effects of habitat fragmentation for 157 nonvolant terrestrial mammal species in Madagascar, one of the biodiversity hotspots with the highest rates of habitat loss and fragmentation. We refined species' geographic ranges based on habitat preferences and elevation limits and then estimated which habitat fragments were too small to support a population for at least 100 years given stochastic population fluctuations. We also evaluated whether time-lagged effects would change the threat status of species according to the International Union for the Conservation of Nature (IUCN) Red List assessment framework. We used allometric relationships to obtain the population parameters required to simulate the population dynamics of each species, and we quantified the consequences of uncertainty in these parameter estimates by repeating the analyses with a range of plausible parameter values. Based on the median outcomes, we found that for 34 species (22% of the 157 species) at least 10% of their current habitat contained unviable populations. Eight species (5%) had a higher threat status when accounting for time-lagged effects. Based on 0.95-quantile values, following a precautionary principle, for 108 species (69%) at least 10% of their habitat contained unviable populations, and 51 species (32%) had a higher threat status. Our results highlight the need to preserve continuous habitat and improve connectivity between habitat fragments. Moreover, our findings may help to identify species for which time-lagged effects are most severe and which may thus benefit the most from conservation actions.

La biodiversidad se encuentra seriamente amenazada por la destrucción del hábitat. Como consecuencia de esta destrucción, el hábitat remanente se vuelve más fragmentado. Esto resulta en extirpaciones poblacionales retardadas dentro de los fragmentos restantes cuando éstos son muy pequeños para mantener a las poblaciones a largo plazo. Si se ignoran estos efectos retardados, se subestimarán los impactos a largo plazo de la pérdida del hábitat y la fragmentación. Cuantificamos la magnitud de los efectos retardados de la fragmentación del hábitat para 157 especies de mamíferos terrestres no voladores en Madagascar, uno de los puntos calientes de biodiversidad con las tasas más elevadas de pérdida del hábitat y fragmentación. Depuramos las extensiones geográficas de las especies con base en las preferencias de hábitat y los límites de elevación y después estimamos cuáles fragmentos de hábitat eran muy pequeños para mantener una población durante al menos cien años dadas las fluctuaciones estocásticas de las poblaciones. También analizamos si los efectos retardados cambiarían el estado de amenaza de la especie de acuerdo con el programa de evaluación de la Lista Roja de la UICN. Usamos relaciones alométricas para obtener los parámetros poblacionales requeridos para simular las dinámicas poblacionales de cada especie y cuantificamos las consecuencias de la incertidumbre en estos parámetros estimados mediante análisis repetidos con una gama de valores plausibles de los parámetros. Con base en los resultados promedio, descubrimos que para 34 especies (22% de las 157 especies) al menos el 10% de su hábitat actual tiene poblaciones inviables. Ocho especies (5%) cambiaron a un estado más elevado de amenaza cuando se consideraron los efectos retardados. Con base en los valores del centil 0.95, adherido a un principio precautorio, para 108 especies (32%) al menos el 10% de su hábitat tiene poblaciones inviables y 51 especies (32%) cambiaron negativamente su estado de amenaza. Nuestros resultados resaltan la necesidad de conservar la continuidad de los hábitats y mejorar la conectividad entre los fragmentos. Además, nuestros hallazgos pueden ayudar a identificar especies para las cuales los efectos retardados son más serios y que podrían beneficiarse más con las acciones de conservación.

Rapid Anthropocene realignment of allometric scaling rules.

The negative relationship between body size and population density in mammals is often interpreted as resulting from energetic constraints. In a global change scenario, however, this relationship might be expected to change, given the size-dependent nature of anthropogenic pressures and vulnerability to extinction. Here we test whether the size-density relationship (SDR) in mammals has changed over the last 50 years. We show that the relationship has shifted down and became shallower, corresponding to a decline in population density of 31-73%, for the largest and smallest mammals, respectively. However, the SDRs became steeper in some groups (e.g. carnivores) and shallower in others (e.g. herbivores). The Anthropocene reorganisation of biotic systems is apparent in macroecological relationships, reinforcing the notion that biodiversity pattens are contingent upon conditions at the time of investigation. We call for an increased attention to the role of global change on macroecological inferences.

Clinical impact of defibrillation testing in a real-world S-ICD population: Data from the ELISIR registry.

BACKGROUND: Current guidelines recommend defibrillation testing (DT) performance in patients with a subcutaneous implantable cardioverter defibrillator (S-ICD), theoretically to reduce the amount of ineffective shocks. DT, however, has been proven unnecessary in transvenous ICD and real-world data show a growing trend in avoidance of DT after S-ICD implantation. METHODS: All patients undergoing S-ICD implant at nine associated Italian centers joining in the ELISIR registry (ClinicalTrials.gov Identifier: NCT04373876) were enrolled and classified upon DT performance. Long-term follow-up events were recorded and compared to report the long-term efficacy and safety of S-ICD implantations without DT in a real-world setting. RESULTS: A total of 420 patients (54.0 ± 15.5 years, 80.0% male) were enrolled in the study. A DT was performed in 254 (60.5%) patients (DT+ group), while in 166 (39.5%) was avoided (DT- group). Over a median follow-up of 19 (11-31) months, a very low rate (0.7%) of ineffective shocks was observed, and no significant differences in the primary combined arrhythmic outcome were observed between the two groups (p = .656). At regression analysis, the only clinical predictor associated with the primary combined outcome was S-ICD placement for primary prevention (odds ratio: 0.42; p = .013); DT performance instead was not associated with a reduction in primary outcome (p = .375). CONCLUSION: Implanting an S-ICD without DT does not appear to impact the safety of defibrillation therapy and overall patients' survival.

Mammal assemblage composition predicts global patterns in emerging infectious disease risk.

As a source of emerging infectious diseases, wildlife assemblages (and related spatial patterns) must be quantitatively assessed to help identify high-risk locations. Previous assessments have largely focussed on the distributions of individual species; however, transmission dynamics are expected to depend on assemblage composition. Moreover, disease-diversity relationships have mainly been studied in the context of species loss, but assemblage composition and disease risk (e.g. infection prevalence in wildlife assemblages) can change without extinction. Based on the predicted distributions and abundances of 4466 mammal species, we estimated global patterns of disease risk through the calculation of the community-level basic reproductive ratio R0, an index of invasion potential, persistence, and maximum prevalence of a pathogen in a wildlife assemblage. For density-dependent diseases, we found that, in addition to tropical areas which are commonly viewed as infectious disease hotspots, northern temperate latitudes included high-risk areas. We also forecasted the effects of climate change and habitat loss from 2015 to 2035. Over this period, many local assemblages showed no net loss of species richness, but the assemblage composition (i.e. the mix of species and their abundances) changed considerably. Simultaneously, most areas experienced a decreased risk of density-dependent diseases but an increased risk of frequency-dependent diseases. We further explored the factors driving these changes in disease risk. Our results suggest that biodiversity and changes therein jointly influence disease risk. Understanding these changes and their drivers and ultimately identifying emerging infectious disease hotspots can help health officials prioritize resource distribution.

Conditional love? Co-occurrence patterns of drought-sensitive species in European grasslands are consistent with the stress-gradient hypothesis.

AIM: The stress-gradient hypothesis (SGH) postulates that species interactions shift from negative to positive with increasing abiotic stress. Interactions between species are increasingly being recognized as important drivers of species distributions, but it is still unclear whether stress-induced changes in interactions affect continental-to-global scale species distributions. Here, we tested whether associations of vascular plant species in dry grasslands in Europe follow the SGH along a climatic water deficit (CWD) gradient across the continent. LOCATION: Dry grasslands in Europe. TIME PERIOD: Present. MAJOR TAXA STUDIED: Vascular plants. METHODS: We built a context-dependent joint species distribution model (JSDM) to estimate the residual associations (i.e., associations that are not explained by the abiotic environment) of 161 plant species as a function of the CWD based on community data from 8,660 vegetation plots. We evaluated changes in residual associations between species for pairs and on the community level, and we compared responses for groups of species with different drought tolerances. RESULTS: We found contrasting shifts in associations for drought-sensitive and drought-tolerant species. For drought-sensitive species, 21% of the pairwise associations became more positive with increasing CWD, whereas 17% became more negative. In contrast, only 17% of the pairwise associations involving drought-tolerant species became more positive, whereas 27% became more negative in areas with a high CWD. Additionally, the incidence of positive associations increased with drought for drought-sensitive species and decreased for drought-tolerant species. MAIN CONCLUSIONS: We found that associations of drought-sensitive plant species became more positive with drought, in line with the SGH. In contrast, associations of drought-tolerant species became more negative. Additionally, changes in associations of single species pairs were highly variable. Our results indicate that stress-modulated species associations might influence the distribution of species over large geographical extents, thus leading to unexpected responses under climate change through shifts in species associations.

The role of brain size on mammalian population densities.

The local abundance or population density of different organisms often varies widely. Understanding what determines this variation is an important, but not yet fully resolved question in ecology. Differences in population density are partly driven by variation in body size and diet among organisms. Here we propose that the size of an organism' brain could be an additional, overlooked, driver of mammalian population densities. We explore two possible contrasting mechanisms by which brain size, measured by its mass, could affect population density. First, because of the energetic demands of larger brains and their influence on life history, we predict mammals with larger relative brain masses would occur at lower population densities. Alternatively, larger brains are generally associated with a greater ability to exploit new resources, which would provide a competitive advantage leading to higher population densities among large-brained mammals. We tested these predictions using phylogenetic path analysis, modelling hypothesized direct and indirect relationships between diet, body mass, brain mass and population density for 656 non-volant terrestrial mammalian species. We analysed all data together and separately for marsupials and the four taxonomic orders with most species in the dataset (Carnivora, Cetartiodactyla, Primates, Rodentia). For all species combined, a single model was supported showing lower population density associated with larger brains, larger bodies and more specialized diets. The negative effect of brain mass was also supported for separate analyses in Primates and Carnivora. In other groups (Rodentia, Cetartiodactyla and marsupials) the relationship was less clear: supported models included a direct link from brain mass to population density but 95% confidence intervals of the path coefficients overlapped zero. Results support our hypothesis that brain mass can explain variation in species' average population density, with large-brained species having greater area requirements, although the relationship may vary across taxonomic groups. Future research is needed to clarify whether the role of brain mass on population density varies as a function of environmental (e.g. environmental stability) and biotic conditions (e.g. level of competition).

Clinical and economic impact of multipoint left ventricular pacing: A comparative analysis from the Italian registry on multipoint pacing in cardiac resynchronization therapy (IRON-MPP).

INTRODUCTION: Early evidence suggests that multipoint left ventricular pacing (MPP) may improve response to cardiac resynchronization therapy (CRT). It is unknown whether this benefit is sustained and cost-effective. We used real-world data to evaluate long-term impact of MPP-ON clinical status, heart failure hospitalizations (HFH) and costs. METHODS: The Italian registry on multipoint left ventricular pacing is a prospective, multicenter registry of patients implanted with MPP-enabled CRT devices. For this analysis, clinical and echocardiographic data were collected through 24 months and compared between patients with (MPP-ON) or without (MPP-OFF) early MPP activation at implant. The total cost of each HFH was estimated with national Italian reimbursement rates. RESULTS: The study included 190 MPP-OFF and 128 MPP-ON patients with similar baseline characteristics. At 1 and 2 years, the MPP-ON group had lower rates of HFH vs MPP-OFF (1-year hazard ratio [HR]: 0.14, P = .0014; 2-year HR: 0.38, P = .009). The finding persisted in a subgroup of patients with consistent MPP activation through follow-up (1-year HR: 0.19; P = .0061; 2-year HR: 0.39, P = .022). Total HFH per-patient costs were lower in the MPP-ON vs the MPP-OFF group at 1 year (€101 ± 50 vs €698 ± 195, P < .001) and 2 years (€366 ± 149 vs €801 ± 203, P = .038). More MPP-ON patients had ≥5% improvement in ejection fraction (76.8% vs 65.4%, P = .025) and clinical composite score (66.7% vs 47.5%, P = .01). CONCLUSIONS: In this multicenter clinical study, early MPP activation was associated with a significant reduction in cumulative HFH and related costs after 1 and 2 years of follow-up.