共查询到20条相似文献，搜索用时 15 毫秒
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Maria João Paúl Dan Rosauer Pedro Tarroso Guillermo Velo-Antón Sílvia B. Carvalho 《Ecology and evolution》2023,13(1):e9666
Understanding the ecological and evolutionary processes driving biodiversity patterns and allowing their persistence is of utmost importance. Many hypotheses have been proposed to explain spatial diversity patterns, including water-energy availability, habitat heterogeneity, and historical climatic refugia. The main goal of this study is to identify if general spatial drivers of species diversity patterns of phylogenetic diversity (PD) and phylogenetic endemism (PE) at the global scale are also predictive of PD and PE at regional scales, using Iberian amphibians as a case study. Our main hypothesis assumes that topography along with contemporary and historical climate are drivers of phylogenetic diversity and endemism, but that the strength of these predictors may be weaker at the regional scale than it tends to be at the global scale. We mapped spatial patterns of Iberian amphibians' phylogenetic diversity and endemism, using previously published phylogenetic and distribution data. Furthermore, we compiled spatial data on topographic and climatic variables related to the water-energy availability, topography, and historical climatic instability hypotheses. To test our hypotheses, we used Spatial Autoregressive Models and selected the best model to explain diversity patterns based on Akaike Information Criterion. Our results show that, out of the variables tested in our study, water-energy availability and historical climate instability are the most important drivers of amphibian diversity in Iberia. However, as predicted, the strength of these predictors in our case study is weaker than it tends to be at global scales. Thus, additional drivers should also be investigated and we suggest caution when interpreting these predictors as surrogates for different components of diversity. 相似文献
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Ingolf Kühn 《Diversity & distributions》2007,13(1):66-69
Though still often neglected, spatial autocorrelation can be a serious issue in ecology because the presence of spatial autocorrelation may alter the parameter estimates and error probabilities of linear models. Here I re-analysed data from a previous study on the relationship between plant species richness and environmental correlates in Germany. While there was a positive relationship between native plant species richness and an altitudinal gradient when ignoring the presence of spatial autocorrelation, the use of a spatial simultaneous liner error model revealed a negative relationship. This most dramatic effect where the observed pattern was inverted may be explained by the environmental situation in Germany. There the highest altitudes are in the south and the lowlands in the north that result in some locally or regionally inverted patterns of the large-scale environmental gradients from the equator to the north. This study therefore shows the necessity to consider spatial autocorrelation in spatial analyses. 相似文献
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Shawna J. Dark 《Diversity & distributions》2004,10(1):1-9
The spatial distribution of invasive alien plants has been poorly documented in California. However, with the increased availability of GIS software and spatially explicit data, the distribution of invasive alien plants can be explored. Using bioregions as defined in Hickman (1993 ), I compared the distribution of invasive alien plants (n = 78) and noninvasive alien plants (n = 1097). The distribution of both categories of alien plants was similar with the exception of a higher concentration of invasive alien plants in the North Coast bioregion. Spatial autocorrelation analysis using Moran's I indicated significant spatial dependence for both invasive and noninvasive alien plant species. I used both ordinary least squares (OLS) and spatial autoregressive (SAR) models to assess the relationship between alien plant species distribution and native plant species richness, road density, population density, elevation, area of sample unit, and precipitation. The OLS model for invasive alien plants included two significant effects; native plant species richness and elevation. The SAR model for invasive alien plants included three significant effects; elevation, road density, and native plant species richness. The SAR model for noninvasive alien plants resulted in the same significant effects as invasive alien plants. Both invasive and noninvasive alien plants are found in regions with low elevation, high road density, and high native‐plant species richness. This is in congruity with previous spatial pattern studies of alien plant species. However, the similarity in effects for both categories of alien plants alludes to the importance of autecological attributes, such as pollination system, dispersal system and differing responses to disturbance in the distribution of invasive plant species. In addition, this study emphasizes the critical importance of testing for spatial autocorrelation in spatial pattern studies and using SAR models when appropriate. 相似文献
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Site occupancy‐detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false‐positive and false‐negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true‐/false‐positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true‐/false‐positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs. 相似文献
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Particulate matter (PM) has been linked to a range of serious cardiovascular and respiratory health problems, including premature mortality. The main objective of our research is to quantify uncertainties about the impacts of fine PM exposure on mortality. We develop a multivariate spatial regression model for the estimation of the risk of mortality associated with fine PM and its components across all counties in the conterminous United States. We characterize different sources of uncertainty in the data and model the spatial structure of the mortality data and the speciated fine PM. We consider a flexible Bayesian hierarchical model for a space-time series of counts (mortality) by constructing a likelihood-based version of a generalized Poisson regression model that combines methods for point-level misaligned data and change of support regression. Our results seem to suggest an increase by a factor of two in the risk of mortality due to fine particles with respect to coarse particles. Our study also shows that in the Western United States, the nitrate and crustal components of the speciated fine PM seem to have more impact on mortality than the other components. On the other hand, in the Eastern United States, sulfate and ammonium explain most of the fine PM effect. 相似文献
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Disease incidence or mortality data are typically available as rates or counts for specified regions, collected over time. We propose Bayesian nonparametric spatial modeling approaches to analyze such data. We develop a hierarchical specification using spatial random effects modeled with a Dirichlet process prior. The Dirichlet process is centered around a multivariate normal distribution. This latter distribution arises from a log-Gaussian process model that provides a latent incidence rate surface, followed by block averaging to the areal units determined by the regions in the study. With regard to the resulting posterior predictive inference, the modeling approach is shown to be equivalent to an approach based on block averaging of a spatial Dirichlet process to obtain a prior probability model for the finite dimensional distribution of the spatial random effects. We introduce a dynamic formulation for the spatial random effects to extend the model to spatio-temporal settings. Posterior inference is implemented through Gibbs sampling. We illustrate the methodology with simulated data as well as with a data set on lung cancer incidences for all 88 counties in the state of Ohio over an observation period of 21 years. 相似文献
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Population-level disease risk varies between communities, and public health professionals are interested in mapping this spatial variation to monitor the locations of high-risk areas and the magnitudes of health inequalities. Almost all of these risk maps relate to a single severity of disease outcome, such as hospitalization, which thus ignores any cases of disease of a different severity, such as a mild case treated in a primary care setting. These spatially-varying risk maps are estimated from spatially aggregated disease count data, but the set of areal units to which these disease counts relate often varies by severity. Thus, the statistical challenge is to provide spatially comparable inference from multiple sets of spatially misaligned disease count data, and an additional complexity is that the spatial extents of the areal units for some severities are partially unknown. This paper thus proposes a novel spatial realignment approach for multivariate misaligned count data, and applies it to the first study delivering spatially comparable inference for multiple severities of the same disease. Inference is via a novel spatially smoothed data augmented MCMC algorithm, and the methods are motivated by a new study of respiratory disease risk in Scotland in 2017. 相似文献
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Aim In their recent paper, Kissling & Carl (2008 ) recommended the spatial error simultaneous autoregressive model (SARerr ) over ordinary least squares (OLS) for modelling species distribution. We compared these models with the generalized least squares model (GLS) and a variant of SAR (SARvario ). GLS and SARvario are superior to standard implementations of SAR because the spatial covariance structure is described by a semivariogram model.
Innovation We used the complete datasets employed by Kissling & Carl (2008 ), with strong spatial autocorrelation, and two datasets in which the spatial structure was degraded by sample reduction and grid coarsening. GLS performed consistently better than OLS, SARerr and SARvario in all datasets, especially in terms of goodness of fit. SARvario was marginally better than SARerr in the degraded datasets.
Main conclusions GLS was more reliable than SAR-based models, so its use is recommended when dealing with spatially autocorrelated data. 相似文献
Innovation We used the complete datasets employed by Kissling & Carl (2008 ), with strong spatial autocorrelation, and two datasets in which the spatial structure was degraded by sample reduction and grid coarsening. GLS performed consistently better than OLS, SAR
Main conclusions GLS was more reliable than SAR-based models, so its use is recommended when dealing with spatially autocorrelated data. 相似文献
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This article reviews recent developments in Bayesian algorithms that explicitly include geographical information in the inference of population structure. Current models substantially differ in their prior distributions and background assumptions, falling into two broad categories: models with or without admixture. To aid users of this new generation of spatially explicit programs, we clarify the assumptions underlying the models, and we test these models in situations where their assumptions are not met. We show that models without admixture are not robust to the inclusion of admixed individuals in the sample, thus providing an incorrect assessment of population genetic structure in many cases. In contrast, admixture models are robust to an absence of admixture in the sample. We also give statistical and conceptual reasons why data should be explored using spatially explicit models that include admixture. 相似文献
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《Developmental cell》2022,57(10):1271-1283.e4
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Pygmy blue whales ( Balaenoptera musculus brevicauda ) are ≤24.1 m and are generally found north of 52°S in summer, whereas the more southerly Antarctic blue whales ( B. m. intermedia ) may exceed 30 m. Previous assessments have assumed that catches and recent surveys south of 60°S recorded Antarctic blue whales, but these may have included pygmy blue whales. Here, we use ovarian corpora, which accumulate with ovulations and hence with length, to separate these subspecies. The resulting Bayesian mixture model, applied to 1,380 Northern Region (north of 52°S and 35°–180°E) and 3,844 Southern Ocean (south of 52°S) blue whales, estimated that only 0.1% (95% credibility intervals 0.0%–0.4%) of the Antarctic region blue whales were pygmy blue whales and, unexpectedly, found significantly lower lifetime ovulation counts for pygmy blue whales than for Antarctic blue whales (7.6 vs . 13.6). Over four decades, despite substantial depletion of Antarctic blue whales, there was no trend in the estimated proportion of pygmy blue whales in the Antarctic. Several lines of investigation found no evidence for sizeable numbers of pygmy blue whales in ovarian corpora data collected in the 1930s, as was previously hypothesized. 相似文献
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A. M. Latimer S. Banerjee H. Sang Jr E. S. Mosher J. A. Silander Jr 《Ecology letters》2009,12(2):144-154
Many critical ecological issues require the analysis of large spatial point data sets – for example, modelling species distributions, abundance and spread from survey data. But modelling spatial relationships, especially in large point data sets, presents major computational challenges. We use a novel Bayesian hierarchical statistical approach, 'spatial predictive process' modelling, to predict the distribution of a major invasive plant species, Celastrus orbiculatus , in the northeastern USA. The model runs orders of magnitude faster than traditional geostatistical models on a large data set of c . 4000 points, and performs better than generalized linear models, generalized additive models and geographically weighted regression in cross-validation. We also use this approach to model simultaneously the distributions of a set of four major invasive species in a spatially explicit multivariate model. This multispecies analysis demonstrates that some pairs of species exhibit negative residual spatial covariation, suggesting potential competitive interaction or divergent responses to unmeasured factors. 相似文献
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Mixture modeling is a popular approach to accommodate overdispersion, skewness, and multimodality features that are very common for health care utilization data. However, mixture modeling tends to rely on subjective judgment regarding the appropriate number of mixture components or some hypothesis about how to cluster the data. In this work, we adopt a nonparametric, variational Bayesian approach to allow the model to select the number of components while estimating their parameters. Our model allows for a probabilistic classification of observations into clusters and simultaneous estimation of a Gaussian regression model within each cluster. When we apply this approach to data on patients with interstitial lung disease, we find distinct subgroups of patients with differences in means and variances of health care costs, health and treatment covariates, and relationships between covariates and costs. The subgroups identified are readily interpretable, suggesting that this nonparametric variational approach to inference can discover valid insights into the factors driving treatment costs. Moreover, the learning algorithm we employed is very fast and scalable, which should make the technique accessible for a broad range of applications. 相似文献
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空间转录物组学是在单细胞RNA测序技术基础上实现细胞空间位置信息测定的组学技术。该技术克服了单细胞转录物组学在单细胞分离建库过程中丢失细胞在组织中空间信息的问题,可同时提供研究对象的转录物组数据信息和在组织中的空间位置信息。空间转录物组学技术对研究细胞谱系的发生过程、细胞间的调控机制和相互作用等具有重要作用,是组学技术研究的重要发展方向和热点。近年来,空间转录物组学技术发展迅速,新的检测方法不断产生,检测灵敏度、分辨率和检测通量等技术指标不断提升。本文根据获取空间信息的原理不同,将较为常用的空间转录物组学技术进行了分类,总结了各类方法的检测原理、代表性技术手段及其相应的技术指标。随后,从脑细胞类型区分与细胞层图谱构建、神经系统相关疾病特征分析与标志物研究两个方面举例论述了空间转录物组学技术在神经科学中的应用。最后,对空间转录物组学技术目前存在的问题进行了总结,并对其未来的发展方向进行了展望。 相似文献
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植物细胞基因表达的异质性是造成组织功能差异的关键因素。近年来,利用空间转录组测序技术研究植物特定生物学问题已取得较大突破,现已成功应用于细胞发育、细胞鉴定和抗逆应激等方面。为探究空间转录组测序技术在植物中的应用,本文对空间转录组测序技术的发展、在植物中的应用以及未来研究方向3个方面进行综述,系统阐述了空间转录组测序技术的发展进程,重点分析其在植物细胞生长和分化、植物细胞鉴定、抗逆应激的应用中所取得的进展。此外,总结空间转录组测序技术在植物应用中存在的挑战,提出未来在植物研究中的方向以及结合其他组学技术的优势,以期利用空间转录组测序技术解决更多植物领域的科学问题。 相似文献
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Colin M. Beale Jack J. Lennon Jon M. Yearsley Mark J. Brewer David A. Elston 《Ecology letters》2010,13(2):246-264
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Bastien Trächsel Valentin Rousson Jean-Luc Bulliard Isabella Locatelli 《Biometrical journal. Biometrische Zeitschrift》2023,65(7):2200046
This study compares the performance of statistical methods for predicting age-standardized cancer incidence, including Poisson generalized linear models, age-period-cohort (APC) and Bayesian age-period-cohort (BAPC) models, autoregressive integrated moving average (ARIMA) time series, and simple linear models. The methods are evaluated via leave-future-out cross-validation, and performance is assessed using the normalized root mean square error, interval score, and coverage of prediction intervals. Methods were applied to cancer incidence from the three Swiss cancer registries of Geneva, Neuchatel, and Vaud combined, considering the five most frequent cancer sites: breast, colorectal, lung, prostate, and skin melanoma and bringing all other sites together in a final group. Best overall performance was achieved by ARIMA models, followed by linear regression models. Prediction methods based on model selection using the Akaike information criterion resulted in overfitting. The widely used APC and BAPC models were found to be suboptimal for prediction, particularly in the case of a trend reversal in incidence, as it was observed for prostate cancer. In general, we do not recommend predicting cancer incidence for periods far into the future but rather updating predictions regularly. 相似文献