Effects of alternative sets of climatic predictors on species distribution models and associated estimates of extinction risk: a test with plants in an arid environment

Aim To evaluate the effects of using distinct alternative sets of climatic predictor variables on the performance, spatial predictions and future projections of species distribution models (SDMs) for rare plants in an arid environment. . Location Atacama and Peruvian Deserts, South America (18º30'S - 31º30'S, 0 - 3 000 m) Methods We modelled the present and future potential distributions of 13 species of Heliotropium sect. Cochranea, a plant group with a centre of diversity in the Atacama Desert. We developed and applied a sequential procedure, starting from climate monthly variables, to derive six alternative sets of climatic predictor variables. We used them to fit models with eight modelling techniques within an ensemble forecasting framework, and derived climate change projections for each of them. We evaluated the effects of using these alternative sets of predictor variables on performance, spatial predictions and projections of SDMs using Generalised Linear Mixed Models (GLMM). Results The use of distinct sets of climatic predictor variables did not have a significant effect on overall metrics of model performance, but had significant effects on present and future spatial predictions. Main conclusion Using different sets of climatic predictors can yield the same model fits but different spatial predictions of current and future species distributions. This represents a new form of uncertainty in model-based estimates of extinction risk that may need to be better acknowledged and quantified in future SDM studies.

Immigration and housing: rethinking the role of fiscal policies to manage diversity

We study the two key social issues of immigration and housing in lightof each other and analyse which housing policies work best to distributediversity (racial, economic, cultural) equally across our cities and towns. Inparticular, we compare the impact of direct government expenditure andtax incentives on the housing conditions of immigrants in four Europeancountries: France, Germany, Spain and the United Kingdom. The analysisshows that the different policies which have been adopted in these countrieshave not succeeded in preventing immigrants from being concentratedin certain neighbourhoods. The reason is that housing benefits andtax incentives are normally “spatially blind”. In our opinion, governmentsshould consider immigration indirectly in their housing policies and, forinstance, distribute social housing more evenly across different areas topromote sustainable levels of diversity.

A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.

1980 Census of population & housing: Iowa by place. Report 1A, Population characteristics, 1982

As with the 1970 Census, the U.S. Department of labor's Employment and Training Administration (ETA) has compiled a series of special reports for the use of program managers and other social scientists concerned with human resources. These reports. which were designed cooperatively by federal, state and local government research staff, include much unpublished data from the 1980 Census Summary Tape Files. The reports in this series cover not only all of the major government and census designated geographic areas in the United States, but also the unique administrative areas that concern program managers.

Genotype and environment interaction on soybean yield in Mato Grosso State, Brazil

The objective of this work was to investigate the genotype-environment interaction in Mato Grosso State, MT. The relative importance of locations, years, sowing dates and cultivars and their interactions was analyzed from data collected in regional yield trials performed in a randomized complete block design with four replications, from 1994-1995 through 1999-2000, in nine locations and two sowing dates. Individual and pooled analyses of variance over years and locations were performed. Complementary analyses of variances partitioned MT State in two main and five smaller regions, respectively: North and South of Cuiabá; and MT-South-A (Pedra Preta area), MT-South-B (Rondonópolis and Itiquira), MT-East (Primavera do Leste and Campo Verde), MT-Central (Nova Mutum, Lucas do Rio Verde and Sorriso) and MT-Parecis (Campo Novo dos Parecis and Sapezal). Locations are relatively more important than years for yield testing soybeans in the MT State, therefore, investment should be made in increasing locations rather than years to improve experimental precision. Partitioning the MT State into regions has little impact on soybean yield testing results and, consequently, on the efficiency of the soybean breeding program in the State. Breeding genotypes with broad adaptation for the MT State is an efficient strategy for cultivar development.

The path: sustainable development in conjunction with meeting the demands of a changing environment.

In the context of severe economic recession, the Library is compelled to adapt to this changing environment, in order to meet the requirements and demands of users with very specific needs. Taking the pillars of sustainable development as a reference point, and extrapolating them to our domain, we establish the next main goals

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

Sensing the environment : a role for the mouse olfactory subsystems

Summary : Four distinct olfactory subsystems compose the mouse olfactory system, the main olfactory epithelium (MOE), the septal organ of Masera (SO), the vomeronasal organ (VNO) and the Grueneberg ganglion (GG). They are implicated in the sensory modalities of the animal and they evolved to analyse and discriminate molecules carrying chemical messages, such as odorants and pheromones. In this thesis, the VNO, principally implicated in pheromonal communications as well as the GG, which had no function attributed until this work, were investigated from their morphology to their physiological functions, using an array of biochemical and physiological methods. First, the roles of a particular protein, the CNGA4 ion channel, were investigated in the VNO. In the MOE, CNGA4 is expressed as a modulatory channel subunit implicated in odour discrimination and adaptation. Interestingly, this calcium channel is the unique member of the cyclic nucleotide-gated (CNG) family to be expressed in the VNO and up to this work its functions remained unknown. Using a combination of transgenic and knockout mice, as well as histological and physiological approaches, we have characterized CNGA4 expression in the VNO. A strong expression in immature neurons was found as well as in the microvilli of mature neurons (putative site of chemodetection). Interestingly and confirming its dual localisation, the genetic invalidation of the CNGA4 channel has, as consequences, a strong impairment in vomeronasal maturation as well as deficit in pheromone sensing. Thus the CNGA4 channel appears to be a multifunctional protein in the mouse VNO playing essential role(s) in this organ. During the second part of the work, the morphology of the most recently described olfactory subsystem, the Grueneberg ganglion, was investigated in detail. Interestingly we found that glial cells and ciliated neurons compose this olfactory ganglion. This particular morphological aspect was similar to the olfactory AWC neurons from C. elegans which was used for further comparisons. Thus as for AWC neurons, we found that GG neurons are sensitive to temperature changes and are able to detect highly volatile molecules. Indeed, the presence of alarm pheromones (APs) secreted by stressed mice, elicit strong cellular responses, as well as a GG dependent behavioural changes. Investigations on the signaling elements present in GG neurons revealed that, as for AWC neurons, or pGC-D expressing neurons from the MOE, proteins participating in a cGMP pathway were found in GG neurons such as pGC-G and CNGA3 channels. These two proteins might be implicated in chemosensing as well as in thermosensing, two apparent properties of this organ. In this thesis, the multisensory modalities of two mouse olfactory subsystems were described and are related to a high degree of complexity required for the animal to sense its environment

Providing more informative projections of climate change impact on plant distribution in a mountain environment

Summary Due to their conic shape and the reduction of area with increasing elevation, mountain ecosystems were early identified as potentially very sensitive to global warming. Moreover, mountain systems may experience unprecedented rates of warming during the next century, two or three times higher than that records of the 20th century. In this context, species distribution models (SDM) have become important tools for rapid assessment of the impact of accelerated land use and climate change on the distribution plant species. In my study, I developed and tested new predictor variables for species distribution models (SDM), specific to current and future geographic projections of plant species in a mountain system, using the Western Swiss Alps as model region. Since meso- and micro-topography are relevant to explain geographic patterns of plant species in mountain environments, I assessed the effect of scale on predictor variables and geographic projections of SDM. I also developed a methodological framework of space-for-time evaluation to test the robustness of SDM when projected in a future changing climate. Finally, I used a cellular automaton to run dynamic simulations of plant migration under climate change in a mountain landscape, including realistic distance of seed dispersal. Results of future projections for the 21st century were also discussed in perspective of vegetation changes monitored during the 20th century. Overall, I showed in this study that, based on the most severe A1 climate change scenario and realistic dispersal simulations of plant dispersal, species extinctions in the Western Swiss Alps could affect nearly one third (28.5%) of the 284 species modeled by 2100. With the less severe 61 scenario, only 4.6% of species are predicted to become extinct. However, even with B1, 54% (153 species) may still loose more than 80% of their initial surface. Results of monitoring of past vegetation changes suggested that plant species can react quickly to the warmer conditions as far as competition is low However, in subalpine grasslands, competition of already present species is probably important and limit establishment of newly arrived species. Results from future simulations also showed that heavy extinctions of alpine plants may start already in 2040, but the latest in 2080. My study also highlighted the importance of fine scale and regional. assessments of climate change impact on mountain vegetation, using more direct predictor variables. Indeed, predictions at the continental scale may fail to predict local refugees or local extinctions, as well as loss of connectivity between local populations. On the other hand, migrations of low-elevation species to higher altitude may be difficult to predict at the local scale. Résumé La forme conique des montagnes ainsi que la diminution de surface dans les hautes altitudes sont reconnues pour exposer plus sensiblement les écosystèmes de montagne au réchauffement global. En outre, les systèmes de montagne seront sans doute soumis durant le 21ème siècle à un réchauffement deux à trois fois plus rapide que celui mesuré durant le 20ème siècle. Dans ce contexte, les modèles prédictifs de distribution géographique de la végétation se sont imposés comme des outils puissants pour de rapides évaluations de l'impact des changements climatiques et de la transformation du paysage par l'homme sur la végétation. Dans mon étude, j'ai développé de nouvelles variables prédictives pour les modèles de distribution, spécifiques à la projection géographique présente et future des plantes dans un système de montagne, en utilisant les Préalpes vaudoises comme zone d'échantillonnage. La méso- et la microtopographie étant particulièrement adaptées pour expliquer les patrons de distribution géographique des plantes dans un environnement montagneux, j'ai testé les effets d'échelle sur les variables prédictives et sur les projections des modèles de distribution. J'ai aussi développé un cadre méthodologique pour tester la robustesse potentielle des modèles lors de projections pour le futur. Finalement, j'ai utilisé un automate cellulaire pour simuler de manière dynamique la migration future des plantes dans le paysage et dans quatre scénarios de changement climatique pour le 21ème siècle. J'ai intégré dans ces simulations des mécanismes et des distances plus réalistes de dispersion de graines. J'ai pu montrer, avec les simulations les plus réalistes, que près du tiers des 284 espèces considérées (28.5%) pourraient être menacées d'extinction en 2100 dans le cas du plus sévère scénario de changement climatique A1. Pour le moins sévère des scénarios B1, seulement 4.6% des espèces sont menacées d'extinctions, mais 54% (153 espèces) risquent de perdre plus 80% de leur habitat initial. Les résultats de monitoring des changements de végétation dans le passé montrent que les plantes peuvent réagir rapidement au réchauffement climatique si la compétition est faible. Dans les prairies subalpines, les espèces déjà présentes limitent certainement l'arrivée de nouvelles espèces par effet de compétition. Les résultats de simulation pour le futur prédisent le début d'extinctions massives dans les Préalpes à partir de 2040, au plus tard en 2080. Mon travail démontre aussi l'importance d'études régionales à échelle fine pour évaluer l'impact des changements climatiques sur la végétation, en intégrant des variables plus directes. En effet, les études à échelle continentale ne tiennent pas compte des micro-refuges, des extinctions locales ni des pertes de connectivité entre populations locales. Malgré cela, la migration des plantes de basses altitudes reste difficile à prédire à l'échelle locale sans modélisation plus globale.

Housing in Iowa, 2006

Profile of statistics about housing stock in Iowa.

Housing in Iowa, 2007

Profile of statistics about housing stock in Iowa.

Housing in Iowa, 2008

Profile of statistics about housing stock in Iowa.

Housing in Iowa, 2009

Profile of statistics about housing stock in Iowa.

Housing in Iowa, 2011

Profile of statistics about housing stock in Iowa.