The ability model of emotional intelligence: Searching for valid measures

Current measures of ability emotional intelligence (EI)--including the well-known Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT)--suffer from several limitations, including low discriminant validity and questionable construct and incremental validity. We show that the MSCEIT is largely predicted by personality dimensions, general intelligence, and demographics having multiple R's with the MSCEIT branches up to .66; for the general EI factor this relation was even stronger (Multiple R = .76). As concerns the factor structure of the MSCEIT, we found support for four first-order factors, which had differential relations with personality, but no support for a higher-order global EI factor. We discuss implications for employing the MSCEIT, including (a) using the single branches scores rather than the total score, (b) always controlling for personality and general intelligence to ensure unbiased parameter estimates in the EI factors, and (c) correcting for measurement error. Failure to account for these methodological aspects may severely compromise predictive validity testing. We also discuss avenues for the improvement of ability-based tests.

Predicting future distributions of mountain plants under climate change: does dispersal capacity matter?

Many studies have investigated the impacts that climate change could potentially have on the distribution of plant species, but few have attempted to constrain projections through plant dispersal limitations. Instead, most studies published so far have been using the simplification of considering dispersal as either unlimited or null. However, depending on a species' dispersal capacity, landscape fragmentation, and the rate of climatic change, these assumptions can lead to serious over- or underestimation of a species' future distribution. To quantify the discrepancies between unlimited, realistic, and no dispersal scenarios, we carried out projections of future distribution over the 21st century for 287 mountain plant species in a study area of the Western Swiss Alps. For each species, simulations were run for four dispersal scenarios (unlimited dispersal, no dispersal, realistic dispersal and realistic dispersal with long-distance dispersal events) and under four climate change scenarios. Although simulations accounting for realistic dispersal limitations did significantly differ from those considering dispersal as unlimited or null in terms of projected future distribution, using the unlimited dispersal simplification nevertheless provided good approximations for species extinctions under more moderate climate change scenarios. Overall, simulations accounting for dispersal limitations produced, for our mountainous study area, results that were significantly closer to unlimited dispersal than to no dispersal. Finally, analyzing the temporal pattern of species extinctions over the entire 21st century showed that, due to the possibility of a large number of species shifting their distribution to higher elevation, important species extinctions for our study area might not occur before the 2080-2100 time periods.

Ecological-economic optimization of biodiversity conservation under climate change

Substantial investment in climate change research has led to dire predictions of the impacts and risks to biodiversity. The Intergovernmental Panel on Climate Change fourth assessment report(1) cites 28,586 studies demonstrating significant biological changes in terrestrial systems(2). Already high extinction rates, driven primarily by habitat loss, are predicted to increase under climate change(3-6). Yet there is little specific advice or precedent in the literature to guide climate adaptation investment for conserving biodiversity within realistic economic constraints(7). Here we present a systematic ecological and economic analysis of a climate adaptation problem in one of the world's most species-rich and threatened ecosystems: the South African fynbos. We discover a counterintuitive optimal investment strategy that switches twice between options as the available adaptation budget increases. We demonstrate that optimal investment is nonlinearly dependent on available resources, making the choice of how much to invest as important as determining where to invest and what actions to take. Our study emphasizes the importance of a sound analytical framework for prioritizing adaptation investments(4). Integrating ecological predictions in an economic decision framework will help support complex choices between adaptation options under severe uncertainty. Our prioritization method can be applied at any scale to minimize species loss and to evaluate the robustness of decisions to uncertainty about key assumptions.

Emotional experience in patients awaiting lung transplantation: A qualitative analysis

Emotional reactions in the course of transplantation are often explored through negative emotions (e.g. stress, guilt) or depression and its evaluation. However the emotional reactions in the course of transplantation describing positive and negative emotional experiences have not been comprehensively described. Qualitative semi-structured interviews were conducted shortly after registration on the waiting list with 15 lung patients waiting for an organ coming from a deceased donor. In a qualitative analysis, focussing on the emotional experience of transplantation, a very rich discourse was underlined. The described emotions in the interviews of the patients were related to specific situations, stakes and existential questions. All these emotions help to describe more precisely the very intimate experience of a difficult and stressful situation while awaiting transplantation. It also helps to better understand the impact of the paradoxical situation of transplantation when a person is waiting for an organ, which will improve quality of life and will allow to survive, but which also depends on the end of the life of a donor.

Altered decision-making in multiple sclerosis: a sign of impaired emotional reactivity?

We assessed decision-making capacity and emotional reactivity in 20 patients with multiple sclerosis (MS) and in 16 healthy subjects using the Gambling Task (GT), a model of real-life decision making, and the skin conductance response (SCR). Demographic, neurological, affective, and cognitive parameters were analyzed in MS patients for their effect on decision-making performance. MS patients persisted longer (slope, -3.6%) than the comparison group (slope, -6.4%) in making disadvantageous choices as the GT progressed (p < 0.001), suggesting significant slower learning in MS. Patients with higher Expanded Disability Status Scale scores (EDSS >2.0) showed a different pattern of impairment in the learning process compared with patients with lower functional impairment (EDSS </=2.0). This slower learning was associated with impaired emotional reactivity (anticipatory SCR 3.9 vs 6.1 microSiemens [microS] for patients vs the comparison group, p < 0.0001; post-choice SCR 3.9 vs 6.2 microS, p < 0.0001), but not with executive dysfunction. Impaired emotional dimensions of behavior (assessed using the Dysexecutive Questionnaire, p < 0.002) also correlated with slower learning. Given the considerable consequences that impaired decision making can have on daily life, we suggest that this factor may contribute to handicap and altered quality of life secondary to MS and is dependent on emotional experience. Ann Neurol 2004.

Climate and vegetation history of western Portugal inferred from Albian near-shore deposits (Gale Formation, Lusitanian Basin)

The late Early Cretaceous greenhouse climate has been studied intensively based on proxy data derived essentially from open marine archives. In contrast, information on continental climatic conditions and on the accompanying response of vegetation is relatively scarce, most notably owing to the stratigraphic uncertainties associated with many Lower Cretaceous terrestrial deposits. Here, we present a palynological record from Albian near-shore deposits of the Lusitanian Basin of W Portugal, which have been independently dated using Sr-isotope signals derived from low-Mg oyster shell calcite. Sr-87/Sr-86 values fluctuate between 0.707373 +/- 0.00002 and 0.707456 +/- 0.00003; absolute values and the overall stratigraphic trend match well with the global open marine seawater signature during Albian times. Based on the new Sr-isotope data, existing biostratigraphic assignments of the succession are corroborated and partly revised. Spore-pollen data provide information on the vegetation community structure and are flanked by sedimentological and clay mineralogical data used to infer the overall climatic conditions prevailing on the adjacent continent. Variations in the distribution of climate-sensitive pollen and spores indicate distinct changes in moisture availability across the studied succession with a pronounced increase in hygrophilous spores in late Early Albian times. Comparison with time-equivalent palynofloras from the Algarve Basin of southern Portugal shows pronounced differences in the xerophyte/hygrophyte ratio, interpreted to reflect the effect of a broad arid climate belt covering southern and southeastern Iberia during Early Albian times.

Functional homogenization of bumblebee communities in alpine landscapes under projected climate change

Background: Bumblebees represent an active pollinator group in mountain regions and assure the pollination of many different plant species from low to high elevations. Plant-pollinator interactions are mediated by functional traits. Shift in bumblebee functional structure under climate change may impact plant-pollinator interactions in mountains. Here, we estimated bumblebee upward shift in elevation, community turnover, and change in functional structure under climate change. Method: We sampled bumblebee species at 149 sites along the elevation gradient. We used stacked species distribution models (S-SDMs) forecasted under three climate change scenarios (A2, A1B, RCP3PD) to model the potential distribution of the Bombus species. Furthermore, we used species proboscis length measurements to assess the functional change in bumblebee assemblages along the elevation gradient. Results: We found species-specific response of bumblebee species to climate change. Species differed in their predicted rate of range contraction and expansion. Losers were mainly species currently restricted to high elevation. Under the most severe climate change scenarios (A2), we found a homogenization of proboscis length structure in bumblebee communities along the elevation gradient through the upward colonization of high elevation by species with longer proboscides. Conclusions: Here, we show that in addition to causing the shift in the distribution of bumblebee species, climate change may impact the functional structure of communities. The colonization of high elevation areas by bumblebee species with long proboscides may modify the structure of plant-pollination interaction networks by increasing the diversity of pollination services at high elevation.

Barn Owl (Tyto alba) breeding biology in relation to breeding season climate

Winter weather has a strong influence on Barn Owl (Tyto alba) breeding biology. Here, we analyzed the impacts of weather conditions on reproductive performance during the breeding season using data collected over 22 years in a Swiss Barn Owl population. Variations in rain and temperature during the breeding season played an important role in within-year variation in Barn Owl reproduction. An increase in rainfall during the period from 4 to 2 weeks preceding egg laying had a positive effect on clutch size. In contrast, fledgling body mass was negatively influenced by rainfall during the 24 h preceding the measurements. Finally, ambient temperature during the rearing period was positively associated with brood size at fledging. In conclusion, weather conditions during the breeding season place constraints on Barn Owl reproduction.

Building the niche through time: using 13,000 years of data to predict the effects of climate change on three tree species in Europe

Aim Species distribution models (SDMs) based on current species ranges underestimate the potential distribution when projected in time and/or space. A multi-temporal model calibration approach has been suggested as an alternative, and we evaluate this using 13,000 years of data. Location Europe. Methods We used fossil-based records of presence for Picea abies, Abies alba and Fagus sylvatica and six climatic variables for the period 13,000 to 1000yr bp. To measure the contribution of each 1000-year time step to the total niche of each species (the niche measured by pooling all the data), we employed a principal components analysis (PCA) calibrated with data over the entire range of possible climates. Then we projected both the total niche and the partial niches from single time frames into the PCA space, and tested if the partial niches were more similar to the total niche than random. Using an ensemble forecasting approach, we calibrated SDMs for each time frame and for the pooled database. We projected each model to current climate and evaluated the results against current pollen data. We also projected all models into the future. Results Niche similarity between the partial and the total-SDMs was almost always statistically significant and increased through time. SDMs calibrated from single time frames gave different results when projected to current climate, providing evidence of a change in the species realized niches through time. Moreover, they predicted limited climate suitability when compared with the total-SDMs. The same results were obtained when projected to future climates. Main conclusions The realized climatic niche of species differed for current and future climates when SDMs were calibrated considering different past climates. Building the niche as an ensemble through time represents a way forward to a better understanding of a species' range and its ecology in a changing climate.

Reconstruction Of Past Climate Conditions Over Central Europe From Groundwater Data

Here we present a 30 000 years low-resolution climate record reconstructed from groundwater data. The investigated site is located in the Bohemian Cretaceous Basin, in the corridor between the Scandinavian ice sheet and the Alpine ice field. Noble gas temperatures (NGT), obtained from groundwater data, preserved multicentennial temperature variability and indicated a cooling of at least 5-7 °C during the last glacial maximum (LGM). This is further confirmed by the depleted δ18O and δ2H values at the LGM. High excess air (ΔNe) at the end of the Pleistocene is possibly related to abrupt changes in recharge dynamics due to progression and retreat of ice covers and permafrost. These results agree with the fact that during the LGM permafrost and small glaciers developed in the inner valleys of the Giant Mountains (located in the watershed of the aquifers). A temporal decrease of deuterium excess from the pre-industrial Holocene to present days is linked to an increase of the air temperatures, and probably also to an increase of water pressure at the source region of precipitation over the past few hundred years

Climate change and plant distribution: local models predict high-elevation persistence

Mountain ecosystems will likely be affected by global warming during the 21st century, with substantial biodiversity loss predicted by species distribution models (SDMs). Depending on the geographic extent, elevation range and spatial resolution of data used in making these models, different rates of habitat loss have been predicted, with associated risk of species extinction. Few coordinated across-scale comparisons have been made using data of different resolution and geographic extent. Here, we assess whether climate-change induced habitat losses predicted at the European scale (10x10' grid cells) are also predicted from local scale data and modeling (25x25m grid cells) in two regions of the Swiss Alps. We show that local-scale models predict persistence of suitable habitats in up to 100% of species that were predicted by a European-scale model to lose all their suitable habitats in the area. Proportion of habitat loss depends on climate change scenario and study area. We find good agreement between the mismatch in predictions between scales and the fine-grain elevation range within 10x10' cells. The greatest prediction discrepancy for alpine species occurs in the area with the largest nival zone. Our results suggest elevation range as the main driver for the observed prediction discrepancies. Local scale projections may better reflect the possibility for species to track their climatic requirement toward higher elevations.