ライフサイエンスコーパス: spatial autocorrelation

1 structure (indicated by both thetaPATCH and spatial autocorrelation).

2 ly through differences in watershed size nor spatial autocorrelation.

3 g spatially explicit models that account for spatial autocorrelation.

4 ding well-defined hexagonal structure in the spatial autocorrelation.

5 gression, with random effects to account for spatial autocorrelation.

6 cation, overlap among breeding locations and spatial autocorrelation.

7 ci were analysed using both F-statistics and spatial autocorrelation.

8 spatial data, this effect is referred to as spatial autocorrelation.

9 e flow, mainly driven by fine-scale positive spatial autocorrelations.

10 Spatial autocorrelation analyses of 12 allozyme loci wer

11 Spatial autocorrelation analyses of kinship coefficients

12 Gene maps and spatial autocorrelation analyses suggest that population

13 In spatial autocorrelation analyses, the overall correlogra

14 d significant isolation by distance based on spatial autocorrelation analyses.

15 Both the spatial autocorrelation analysis and estimates from fluo

16 Spatial autocorrelation analysis indicated some evidence

17 patches were deduced from a two-dimensional spatial autocorrelation analysis of NSOM images that res

18 Spatial autocorrelation analysis using Moran's Index det

19 The local Moran's I test for spatial autocorrelation and correlograms assessing space

20 eld data on species richness, host identity, spatial autocorrelation and disease prevalence.

21 Dispersal limitation was suggested by both spatial autocorrelation and the detection of population

22 c size and disease pattern (characterized by spatial autocorrelation), and its dependence on dispersa

23 3D data sets, accounting for effects due to spatial autocorrelation becomes even more indispensable.

24 of these families that eDNA samples overcome spatial autocorrelation biases associated with the class

25 stem, and species distributions with reduced spatial autocorrelation compared to commonly used interp

26 Using a novel algorithm combining spatial autocorrelation detection and principal componen

27 We show that, by accounting for spatial autocorrelation, discovery rates (i.e., the rati

28 We compute the spatial autocorrelation function of the selected genes a

29 The spatial autocorrelation functions of the resulting image

30 l cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the

31 ically according to analyses techniques like spatial autocorrelation, grid scores, and modifiable are

32 000 m to show that bacteria have significant spatial autocorrelation in community composition up to a

33 ixed habitat heterogeneity, increasing local spatial autocorrelation in habitat generally has a benef

34 Our results indicate that spatial autocorrelation in local weather has increased w

35 aerosol precursors are leading to declining spatial autocorrelation in the aerosol fields and increa

36 ffect contiguous blocks of sites, increasing spatial autocorrelation in the disturbances has a harmfu

37 In this study, we investigated spatial autocorrelation in three different matrix-assist

38 estation based on a decomposition of Moran's spatial autocorrelation index; and second, develop a Gau

39 lysis of spatial patterns using a measure of spatial autocorrelation indicated that counties with mos

40 Spatial autocorrelations indicated a significant spatial

41 The minimization of global spatial autocorrelation is used to choose the optimal sp

42 ution of predicted movement properties (i.e. spatial autocorrelation, mean, and variance of RT and Tt

43 Thus the spatial autocorrelation observed is probably not just a

44 Spatial autocorrelation of model residuals indicated tha

45 Finally, we assessed the reproducibility and spatial autocorrelation of nano- and micronscale protein

46 ation in the murid genome by calculating the spatial autocorrelation of nucleotide substitution rates

47 ement between states is thought to limit the spatial autocorrelation of O. nubilalis.

48 Spatial autocorrelation of rates is the propensity of re

49 In landscapes with high spatial autocorrelation of RT and TtoR, a high variation

50 ine, and evidence for short-distance genetic spatial autocorrelation provided evidence of diffusive d

51 highly significant spatial structuring with spatial autocorrelation ranges of 54-133 m, but beyond t

52 measure eye movements, showed no significant spatial autocorrelations, ruling out eye movements as th

53 A spatial autocorrelation (SA) treatment of S locus and al

54 The results show that spatial autocorrelation statistics are highly predictabl

55 characterizes the statistical properties of spatial autocorrelation statistics in this context and d

56 rocesses, can be analyzed in detail by using spatial autocorrelation statistics.

57 We used hierarchical F-statistics and spatial autocorrelation to characterize spatial genetic

58 Positive spatial autocorrelation (two-sided p = 0.001) revealed t

59 recover very quickly from damage; herbivore spatial autocorrelation was always weak.

60 Significant spatial autocorrelation was detected in all three data s

61 tance between pond study sites) and negative spatial autocorrelation was observed at 100-150 km and w

62 Significant spatial autocorrelation was observed for site amino acid

63 Positive spatial autocorrelation was recorded for urban and nonur

64 Using measures of genetic distance and spatial autocorrelation, we compared metrics between sex

65 By using phylogenetic history to correct for spatial autocorrelation, we illustrate how a fundamental

66 Significant spatial autocorrelations were detected over larger scale

67 that plant populations will exhibit internal spatial autocorrelation when propagule flow is restricte

68 as a quasi-regular lattice and analyzed with spatial autocorrelation, yielding parameters that quanti