scholarly journals Local environmental effects and spatial effects in macroecological studies using mapped abundance classes: the case of the rook Corvus frugilegus in Scotland

2006 ◽  
Vol 75 (5) ◽  
pp. 1140-1146 ◽  
Author(s):  
A. GIMONA ◽  
M. J. BREWER
Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 524
Author(s):  
Walguen Oscar ◽  
Jean Vaillant

Cox processes, also called doubly stochastic Poisson processes, are used for describing phenomena for which overdispersion exists, as well as Poisson properties conditional on environmental effects. In this paper, we consider situations where spatial count data are not available for the whole study area but only for sampling units within identified strata. Moreover, we introduce a model of spatial dependency for environmental effects based on a Gaussian copula and gamma-distributed margins. The strength of dependency between spatial effects is related with the distance between stratum centers. Sampling properties are presented taking into account the spatial random field of covariates. Likelihood and Bayesian inference approaches are proposed to estimate the effect parameters and the covariate link function parameters. These techniques are illustrated using Black Leaf Streak Disease (BLSD) data collected in Martinique island.


2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Dengli Tang ◽  
Shijie Li ◽  
Yuanhua Yang ◽  
Lianglie Gu

Environmental pollution has aroused extensive concern worldwide in recent years. Existing studies on the relationship between foreign direct investment (FDI) and environmental pollution have, however, paid little attention to spatial effects and regional corruption’s environmental performance from a spatial perspective. To address this gap, we investigate the spatial agglomeration effects of environmental pollution in China and the environmental effects of FDI and regional corruption using spatial econometric analysis method. The results indicate significant spatial agglomeration effects in environmental pollution. The results of spatial panel data models reveal that the estimation coefficient of FDI is significantly negative, and FDI inflows reduce China’s environmental pollution. Regional corruption is shown to increase environmental pollution, thereby contributing further to environmental degradation. The interaction coefficient of FDI and regional corruption is significantly positive, indicating that regional corruption reduces the environmental benefits derived from FDI. In addition, regional differences in spatial effects verify that regional corruption also reduces the environmental performance of FDI in the central region. Meanwhile, regional corruption increases the environmental aggravation effects of FDI in the eastern region but weakens it in the western region. Our findings lead to some policy recommendations with regard to environmental protection and pollution control.


2020 ◽  
Vol 77 (5) ◽  
pp. 894-903 ◽  
Author(s):  
Sophie Cauvy-Fraunié ◽  
Verena M. Trenkel ◽  
Martin Daufresne ◽  
Anthony Maire ◽  
Hervé Capra ◽  
...  

Long-term ecological surveys (LTES) often exhibit strong variability among sampling dates. The use and interpretation of such interannual variability is challenging due to the combination of multiple processes involved and sampling uncertainty. Here, we analysed the interannual variability in ∼30 years of 150 species density (fish and invertebrate) and environmental observation time series in four aquatic systems (stream, river, estuary, and marine continental shelf) with different sampling efforts to identify the information provided by this variability. We tested, using two empirical methods, whether we could observe simultaneous fluctuation between detrended time series corresponding to widely acknowledged assumptions about aquatic population dynamics: spatial effects, cohort effects, and environmental effects. We found a low number of significant results (36%, 9%, and 0% for spatial, cohort, and environmental effects, respectively), suggesting that sampling uncertainty overrode the effects of biological processes. Our study does not question the relevance of LTES for detecting important trends, but clearly indicates that the statistical power to interpret interannual variations in aquatic species densities is low, especially in large systems where the degree of sampling effort is always limited.


2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Maria L. Selle ◽  
Ingelin Steinsland ◽  
Owen Powell ◽  
John M. Hickey ◽  
Gregor Gorjanc

Abstract Background Breeders and geneticists use statistical models to separate genetic and environmental effects on phenotype. A common way to separate these effects is to model a descriptor of an environment, a contemporary group or herd, and account for genetic relationship between animals across environments. However, separating the genetic and environmental effects in smallholder systems is challenging due to small herd sizes and weak genetic connectedness across herds. We hypothesised that accounting for spatial relationships between nearby herds can improve genetic evaluation in smallholder systems. Furthermore, geographically referenced environmental covariates are increasingly available and could model underlying sources of spatial relationships. The objective of this study was therefore, to evaluate the potential of spatial modelling to improve genetic evaluation in dairy cattle smallholder systems. Methods We performed simulations and real dairy cattle data analysis to test our hypothesis. We modelled environmental variation by estimating herd and spatial effects. Herd effects were considered independent, whereas spatial effects had distance-based covariance between herds. We compared these models using pedigree or genomic data. Results The results show that in smallholder systems (i) standard models do not separate genetic and environmental effects accurately, (ii) spatial modelling increases the accuracy of genetic evaluation for phenotyped and non-phenotyped animals, (iii) environmental covariates do not substantially improve the accuracy of genetic evaluation beyond simple distance-based relationships between herds, (iv) the benefit of spatial modelling was largest when separating the genetic and environmental effects was challenging, and (v) spatial modelling was beneficial when using either pedigree or genomic data. Conclusions We have demonstrated the potential of spatial modelling to improve genetic evaluation in smallholder systems. This improvement is driven by establishing environmental connectedness between herds, which enhances separation of genetic and environmental effects. We suggest routine spatial modelling in genetic evaluations, particularly for smallholder systems. Spatial modelling could also have a major impact in studies of human and wild populations.


2020 ◽  
Author(s):  
Maria L. Selle ◽  
Ingelin Steinsland ◽  
Owen Powell ◽  
John M. Hickey ◽  
Gregor Gorjanc

AbstractBreeders and geneticists use statistical models for genetic evaluation of animals to separate genetic and environmental effects on phenotype. A common way to separate these effects is to model a descriptor of an environment, a contemporary group or herd, and account for genetic relationship between animals across the environments. However, separating the genetic and environmental effects in smallholder systems is challenging due to small herd sizes and weak genetic connectedness across herds. Our hypothesis was that accounting for spatial relationships between nearby herds can improve genetic evaluation in smallholder systems. Further, geographically referenced environmental covariates are increasingly available and could be used to model underlying sources of the spatial relationships. The objective of this study was therefore to evaluate the potential of spatial modelling to improve genetic evaluation in smallholder systems. We focus solely on dairy cattle smallholder systems.We performed simulations and real dairy cattle data analysis to test our hypothesis. We used a range of models to account for environmental variation by estimating herd and spatial effects. We compared these models using pedigree or genomic data.The results show that in smallholder systems (i) standard models are not able to separate genetic and environmental effects, (ii) spatial modelling increases accuracy of genetic evaluation for phenotyped and non-phenotyped animals, (iii) environmental covariates do not substantially improve accuracy of genetic evaluation beyond simple distance-driven spatial relationships between herds, (iv) the benefit of spatial modelling was the largest when the genetic and environmental effects were hard to separate and (v) spatial modelling was beneficial when using either pedigree or genomic data.We have demonstrated the potential of spatial modelling to improve genetic evaluation in smallholder systems. This improvement is driven by establishing environmental connectedness between herds that enhances separation of the genetic and environmental effects. We suggest routine spatial modelling in genetic evaluations, particularly for smallholder systems. Spatial modelling could also have major impact in studies of human and wild populations.


Author(s):  
N.J. Tao ◽  
J.A. DeRose ◽  
P.I. Oden ◽  
S.M. Lindsay

Clemmer and Beebe have pointed out that surface structures on graphite substrates can be misinterpreted as biopolymer images in STM experiments. We have been using electrochemical methods to react DNA fragments onto gold electrodes for STM and AFM imaging. The adsorbates produced in this way are only homogeneous in special circumstances. Searching an inhomogeneous substrate for ‘desired’ images limits the value of the data. Here, we report on a reversible method for imaging adsorbates. The molecules can be lifted onto and off the substrate during imaging. This leaves no doubt about the validity or statistical significance of the images. Furthermore, environmental effects (such as changes in electrolyte or surface charge) can be investigated easily.


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