The small-scale variation of herb-layer community structure in a riparian mixed forest

The ground vegetation layer is the most diverse plant community in forest ecosystems. We have shown the role of spatial variables, soil properties and overstorey structure in spatial variation of the herb-layer community in a riparian mixed forest . The research was conducted in the "Dnipro-Orils’kiy" Nature Reserve (Ukraine). The research polygon was located in the forest in the floodplain of the River Protich, which is a left tributary of the River Dnipro. Plant abundance was quantified by measuring cover within an experimental polygon. The experimental polygon consisted of 7 transects, each comprising 15 test points. The distance between the rows in the site was 3 m. At the site we established a plot of 45 × 21 m, with 105 subplots of 3 × 3 m organized in a regular grid. A list of vascular plant species was composed for each 3 × 3 m subplot along with visual estimates of species cover projection. The plant community was represented by 43 species, of which 18.6% were phanerophytes, 39.5% were hemicryptophytes, 9.3% were therophytes, 7.0% were geophytes. An overall test of random labelling revealed the total nonrandom distribution of the tree stems within the site. The species-specific test of random labelling showed the nonrandom segregated distribution of Acer tataricum, Pyrus communis, Quercus robur, and Ulmus laevis. Crataegus monogyna and Euonymus europaeus were distributed randomly. The nearest neighbour of Acer tataricum was less likely to be Ulmus laevis. There was no direct spatial connection between Acer tataricum and other trees. Crataegus monogyna, Pyrus communis, Quercus robur and Euonymus europaeus were not segregated from all other species. The nearest neighbour of Ulmus laevis was less likely to be Acer tataricum. Constrained correspondence analysis (CCA) was applied as ordination approach. The forward selection procedure allowed us to select 6 soil variables which explain 28.3% of the herb-layer community variability. The list of the important soil variables includes soil mechanical impedance (at the depth 0–5, 30–35, 75–80, and 95–10 cm), soil moisture, and soil bulk density. The variation explained by pure spatial variables accounted for 11.0 %. The majority of the tree-distance structured variation in plant community composition was broad-scaled. The spatial scalograms were left-skewed asymmetric. Significant relationship was found between the pure spatial component of the community variation and a number of phytoindicator estimations, most important of which were the variability of damping and humidity. Tree stand was obseerved to be a considerable factor structuring both the herb-layer community and spatial variation of the physical properties of soil.

Revealing rangeomorph species characters using spatial analyses

Rangeomorphs dominate the Ediacaran Avalonian macrofossil assemblages of Charnwood Forest, UK (∼562 Ma). However, their unfamiliar fractal architecture makes distinguishing phylogenetically reliable characters from intraspecific features difficult. Fortunately, spatial analysis of large in-situ populations offers an independent means of assessing their taxonomy. Populations of a single biological species are likely to exhibit similar spatial distributions due to their shared responses to the biological and ecological processes acting upon them. As such, spatial analyses can be used to interrogate which are the most taxonomically deductive characters in similar species. We used random labelling analyses to investigate the presence or absence of characters of Primocandelabrum boyntoni, P. aethelfalaedia, and P. aelfwynnia on the Bed ‘B’. The resultant spatial distributions were compared to observed characters using goodness-of-fit tests to determine which characters were associated with unique populations, and which were found across multiple populations. We found that P. boyntoni and P. aelfwynnia had statistically indistinguishable character distributions, suggesting that they represent a single biological species, and that they exhibited significantly different distributions to P. aethelfalaedia, suggesting that there are two (rather than three) species of Primocandelabrum present on the B surface. Furthermore, we found that the distribution of concealed versus unconcealed 1st order branches across all specimens exhibited significantly different density-dependant behaviour, with unconcealed branching occurring in areas of higher density populations and concealed branching occurring in the lower density areas of Primocandelabrum. We speculate that unconcealed branches may have been a response to the reduced availability of resources in higher density areas, implying rangeomorphs were capable of ecophenotypic responses.

Correlations in bivariate point processes: some biological applications

Let (Φ1 Φ2) be a bivariate point process. Let be the probability that (Φ1 Φ2) - (0, s) (the process without the 0 and s points) verify U when we have a point of <1>1 in the origin and a point of Φ2 in s. This is the reduced cross Palm distribution. Some correlation measures for bivariate point processes based on this reduced cross Palm distribution are proposed. Their estimators and expressions under the independence and the random labelling hypothesis are considered. The differences and improvements with respect to the cross intensity function and its integrated version, the cross function (Stoyan et al. 1987), are studied. Some Monte Carlo tests for testing the independence and the random labelling hypothesis are proposed. They are applied to real bivariate point patterns: positions of hickories and maples in the Lansing Woods (Diggle 1983) and cases and controls of childhood leukaemia and lymphoma in North Humberside (Cuzick and Edwards 1990).

Correlations in bivariate point processes: some biological applications

Let (Φ1 Φ2) be a bivariate point process. Let be the probability that (Φ1 Φ2) - (0, s) (the process without the 0 and s points) verify U when we have a point of <1>1 in the origin and a point of Φ2 in s. This is the reduced cross Palm distribution. Some correlation measures for bivariate point processes based on this reduced cross Palm distribution are proposed. Their estimators and expressions under the independence and the random labelling hypothesis are considered. The differences and improvements with respect to the cross intensity function and its integrated version, the cross function (Stoyan et al. 1987), are studied. Some Monte Carlo tests for testing the independence and the random labelling hypothesis are proposed. They are applied to real bivariate point patterns: positions of hickories and maples in the Lansing Woods (Diggle 1983) and cases and controls of childhood leukaemia and lymphoma in North Humberside (Cuzick and Edwards 1990).