950 resultados para Species Distribution Modeling
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We used the results of the Spanish Otter Survey of 1994–1996, a Geographic Information System and stepwise multiple logistic regression to model otter presence/absence data in the continental Spanish UTM 10 10-km squares. Geographic situation, indicators of human activity such as highways and major urban centers, and environmental variables related with productivity, water availability, altitude, and environmental energy were included in a logistic model that correctly classified about 73% of otter presences and absences. We extrapolated the model to the adjacent territory of Portugal, and increased the model’s spatial resolution by extrapolating it to 1 1-km squares in the whole Iberian Peninsula. The model turned out to be rather flexible, predicting, for instance, the species to be very restricted to the courses of rivers in some areas, and more widespread in others. This allowed us to determine areas where otter populations may be more vulnerable to habitat changes or harmful human interventions. # 2003 Elsevier Ltd. All rights reserved.
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Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.
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All life-history stages of the Australian Podonominae (Chironomidae) genus Archaeochlus Brundin are revised, providing evidence for recognition of a separate clade, named here as Austrochlus Cranston. Based on molecular and morphological evidence, the clade contains two additional species: Austrochlus parabrundini Cranston, Edward and Cook sp. n. is described from Western Australia where its granite outcrop seepage habitat and geographical range is almost identical to that of the type species Austrochlus brundini Cranston, Edward and Colless (n. comb); Austrochlus centralaustralis Cranston, Edward and Cook sp. n. is described from ephemeral seepage/flows in the MacDonnell and James Ranges of central Australia. Molecular studies reported here confirm species distinctions, relationships to African taxa, and basal relationships within the Chironomidae. Modelled distributions provide evidence for range restriction by seasonal rainfall patterns.
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Copyright © 2014 Entomological Society of America.
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Recent studies have shown differences in the epidemiology of invasive infections caused by Candida species worldwide. In the period comprising August 2002 to August 2003, we performed a study in Santa Casa Complexo Hospitalar, Brazil, to determine Candida species distribution associated with candidemia and their antifungal susceptibility profiles to amphotericin B, fluconazole and itraconazole. Antifungal susceptibility was tested according to the broth microdilution method described in the NCCLS (M27A-2 method). Only one sample from each patient was analyzed (the first isolate). Most of the episodes had been caused by species other than C. albicans (51.6%), including C. parapsilosis (25.8%), C. tropicalis (13.3%), C. glabrata (3.3%), C. krusei (1.7%), and others (7.5%). Dose-dependent susceptibility to itraconazole was observed in 14.2% of strains, and dose-dependent susceptibility to fluconazole was found in 1.6%. Antifungal resistance was not found, probably related to low use of fluconazole. Further epidemiological surveillance is needed.
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Although Candida albicans is the main cause of fungal esophagitis, other species such as C. tropicalis, C. krusei and C. stellatoidea have also been implicated. Several studies have identified risk factors for C. albicans esophagitis. However, data for non-C. albicans species is still sparse. The aim of this study was to determine the etiology of Candida esophagitis in our medical centre over an 18-month period. Additionally, we aimed to investigate predisposing conditions for esophageal candidosis caused by different Candida species. A total of 21,248 upper gastroscopies were performed in Santa Casa Complexo Hospitalar between January 2005 and July 2006. The prevalence of Candida esophagitis was 0.74% (n = 158). C. albicans caused the vast majority of infections (96.2%), followed by C. tropicalis (2.5%), C. lusitaniae (0.6%) and C. glabrata (0.6%). There were 81 women (51.3%) and 77 men (48.7%). No case of mixed infection occurred. Concomitant oral candidosis was documented for 10.8% (n = 17). Most of cases (55.1%) involved outpatients. Around one fifth of patients in our cohort had no identifiable risk factors for esophageal candidosis (20.8%). Since nearly all infections were caused by C. albicans we were not able to determine risk factors for esophagitis caused by other Candida species.
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INTRODUCTION: In this study, we aimed at identifying Candida isolates obtained from blood, urine, tracheal secretion, and nail/skin lesions from cases attended at the Hospital Universitário de Londrina over a 3-year period and at evaluating fluconazole susceptibilities of the isolates. METHODS: Candida isolates were identified by polymerase chain reaction (PCR) using species-specific forward primers. The in vitro fluconazole susceptibility test was performed according to EUCAST-AFST reference procedure. RESULTS: Isolates were obtained from urine (53.4%), blood cultures (19.2%), tracheal secretion (17.8%), and nail/skin lesions (9.6%). When urine samples were considered, prevalence was similar in women (45.5%) and in men (54.5%) and was high in the age group >61 years than that in younger ones. For blood samples, prevalence was high in neonates (35%) and advanced ages (22.5%). For nail and skin samples, prevalence was higher in women (71.4%) than in men (28.6%). Candida albicans was the most frequently isolated in the hospital, but Candida species other than C. albicans accounted for 64% of isolates, including predominantly Candida tropicalis (33.2%) and Candida parapsilosis (19.2%). The trend for non-albicans Candida as the predominant species was noted from all clinical specimens, except from urine samples. All Candida isolates were considered susceptible in vitro to fluconazole with the exception of isolates belonging to the intrinsically less-susceptible species C. glabrata. CONCLUSIONS: Non-albicans Candida species were more frequently isolated in the hospital. Fluconazole resistance was a rare finding in our study.
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We investigate palm species distribution, richness and abundance along the Mokoti, a seasonally-dry river of southeastern Amazon and compare it to the patterns observed at a large scale, comprising the entire Brazilian territory. A total of 694 palms belonging to 10 species were sampled at the Mokoti River basin. Although the species showed diverse distribution patterns, we found that local palm abundance, richness and tree basal area were significantly higher from the hills to the bottomlands of the study region, revealing a positive association of these measures with moisture. The analyses at the larger spatial scale also showed a strong influence of vapor pressure (a measure of moisture content of the air, in turn modulated by temperature) and seasonality in temperature: the richest regions were those where temperature and humidity were simultaneously high, and which also presented a lower degree of seasonality in temperature. These results indicate that the distribution of palms seems to be strongly associated with climatic variables, supporting the idea that, by 'putting all the eggs in one basket' (a consequence of survival depending on the preservation of a single irreplaceable bud), palms have become vulnerable to extreme environmental conditions. Hence, their distribution is concentrated in those tropical and sub-tropical regions with constant conditions of (mild to high) temperature and moisture all year round.
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In this work, I apply a simple protocol to species occurrence inventory of Odonata in a region of Maranhão state which has very few distributional records. Some relations between species occurrence and environmental characteristics are discussed, mainly in relation to the high occurrence of Erythemis. Eighteen new records are presented discussing the role of this approach to generate useful information for conservation purposes.
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ABSTRACT Amphibians are the most threatened vertebrate group according to the IUCN. Land-use and land cover change (LULCC) and climate change (CC) are two of the main factors related to declining amphibian populations. Given the vulnerability of threatened and rare species, the study of their response to these impacts is a conservation priority. The aim of this work was to analyze the combined impact of LULCC and CC on the regionally endemic species Melanophryniscus sanmartini Klappenbach, 1968. This species is currently categorized as near threatened by the IUCN, and previous studies suggest negative effects of projected changes in climate. Using maximum entropy methods we modeled the effects of CC on the current and mid-century distribution of M. sanmartini under two IPCC scenarios - A2 (severe) and B2 (moderate). The effects of LULCC were studied by superimposing the potential distribution with current land use, while future distribution models were evaluated under the scenario of maximum expansion of soybean and afforestation in Uruguay. The results suggest that M. sanmartini is distributed in eastern Uruguay and the south of Brazil, mainly related to hilly and grasslands systems. Currently more than 10% of this species' distribution is superimposed by agricultural crops and exotic forest plantations. Contrasting with a recent modelling study our models suggest an expansion of the distribution of M. sanmartini by mid-century under both climate scenarios. However, despite the rise in climatically suitable areas for the species in the future, LULCC projections indicate that the proportion of modified habitats will occupy up to 25% of the distribution of M. sanmartini. Future change in climate conditions could represent an opportunity for M. sanmartini, but management measures are needed to mitigate the effects of habitat modification in order to ensure its survival and allow the eventual expansion of its distribution.
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Abiotic factors are considered strong drivers of species distribution and assemblages. Yet these spatial patterns are also influenced by biotic interactions. Accounting for competitors or facilitators may improve both the fit and the predictive power of species distribution models (SDMs). We investigated the influence of a dominant species, Empetrum nigrum ssp. hermaphroditum, on the distribution of 34 subordinate species in the tundra of northern Norway. We related SDM parameters of those subordinate species to their functional traits and their co-occurrence patterns with E. hermaphroditum across three spatial scales. By combining both approaches, we sought to understand whether these species may be limited by competitive interactions and/or benefit from habitat conditions created by the dominant species. The model fit and predictive power increased for most species when the frequency of occurrence of E. hermaphroditum was included in the SDMs as a predictor. The largest increase was found for species that 1) co-occur most of the time with E. hermaphroditum, both at large (i.e. 750 m) and small spatial scale (i.e. 2 m) or co-occur with E. hermaphroditum at large scale but not at small scale and 2) have particularly low or high leaf dry matter content (LDMC). Species that do not co-occur with E. hermaphroditum at the smallest scale are generally palatable herbaceous species with low LDMC, thus showing a weak ability to tolerate resource depletion that is directly or indirectly induced by E. hermaphroditum. Species with high LDMC, showing a better aptitude to face resource depletion and grazing, are often found in the proximity of E. hermaphroditum. Our results are consistent with previous findings that both competition and facilitation structure plant distribution and assemblages in the Arctic tundra. The functional and co-occurrence approaches used were complementary and provided a deeper understanding of the observed patterns by refinement of the pool of potential direct and indirect ecological effects of E. hermaphroditum on the distribution of subordinate species. Our correlative study would benefit being complemented by experimental approaches.
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1. Model-based approaches have been used increasingly in conservation biology over recent years. Species presence data used for predictive species distribution modelling are abundant in natural history collections, whereas reliable absence data are sparse, most notably for vagrant species such as butterflies and snakes. As predictive methods such as generalized linear models (GLM) require absence data, various strategies have been proposed to select pseudo-absence data. However, only a few studies exist that compare different approaches to generating these pseudo-absence data. 2. Natural history collection data are usually available for long periods of time (decades or even centuries), thus allowing historical considerations. However, this historical dimension has rarely been assessed in studies of species distribution, although there is great potential for understanding current patterns, i.e. the past is the key to the present. 3. We used GLM to model the distributions of three 'target' butterfly species, Melitaea didyma, Coenonympha tullia and Maculinea teleius, in Switzerland. We developed and compared four strategies for defining pools of pseudo-absence data and applied them to natural history collection data from the last 10, 30 and 100 years. Pools included: (i) sites without target species records; (ii) sites where butterfly species other than the target species were present; (iii) sites without butterfly species but with habitat characteristics similar to those required by the target species; and (iv) a combination of the second and third strategies. Models were evaluated and compared by the total deviance explained, the maximized Kappa and the area under the curve (AUC). 4. Among the four strategies, model performance was best for strategy 3. Contrary to expectations, strategy 2 resulted in even lower model performance compared with models with pseudo-absence data simulated totally at random (strategy 1). 5. Independent of the strategy model, performance was enhanced when sites with historical species presence data were not considered as pseudo-absence data. Therefore, the combination of strategy 3 with species records from the last 100 years achieved the highest model performance. 6. Synthesis and applications. The protection of suitable habitat for species survival or reintroduction in rapidly changing landscapes is a high priority among conservationists. Model-based approaches offer planning authorities the possibility of delimiting priority areas for species detection or habitat protection. The performance of these models can be enhanced by fitting them with pseudo-absence data relying on large archives of natural history collection species presence data rather than using randomly sampled pseudo-absence data.
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In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented.
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The ability to model biodiversity patterns is of prime importance in this era of severe environmental crisis. Species assemblage along environmental gradient is subject to the interplay of biotic interactions in complement to abiotic environmental filtering. Accounting for complex biotic interactions for a wide array of species remains so far challenging. Here, we propose to use food web models that can infer the potential interaction links between species as a constraint in species distribution models. Using a plant-herbivore (butterfly) interaction dataset, we demonstrate that this combined approach is able to improve both species distribution and community forecasts. Most importantly, this combined approach is very useful in rendering models of more generalist species that have multiple potential interaction links, where gap in the literature may be recurrent. Our combined approach points a promising direction forward to model the spatial variation of entire species interaction networks. Our work has implications for studies of range shifting species and invasive species biology where it may be unknown how a given biota might interact with a potential invader or in future climate.