DOI: 10.5061/dryad.brv15dv70
title: Data from: A new digital method of data collection for spatial point pattern analysis in grassland communities

description: A major objective of plant ecology research is to determine the underlying
 processes responsible for the observed spatial distribution patterns of
 plant species. Plants can be approximated as points in space for this
 purpose, and thus, spatial point pattern analysis has become increasingly
 popular in ecological research. The basic piece of data for point pattern
 analysis is a point location of an ecological object in some study region.
 Therefore, point pattern analysis can only be performed if data can be
 collected. However, due to the lack of a convenient sampling method, a few
 previous studies have used point pattern analysis to examine the spatial
 patterns of grassland species. This is unfortunate because being able to
 explore point patterns in grassland systems has widespread implications
 for population dynamics, community-level patterns and ecological
 processes. In this study, we develop a new method to measure individual
 coordinates of species in grassland communities. This method records plant
 growing positions via digital picture samples that have been sub-blocked
 within a geographical information system (GIS). Here, we tested out the
 new method by measuring the individual coordinates of Stipa grandis in
 grazed and ungrazed S. grandis communities in a temperate steppe ecosystem
 in China. Furthermore, we analyzed the pattern of S. grandis by using the
 pair correlation function g(r) with both a homogeneous Poisson process and
 a heterogeneous Poisson process. Our results showed that individuals of S.
 grandis were overdispersed according to the homogeneous Poisson process at
 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m
 according to the homogeneous and heterogeneous Poisson processes in the
 grazed community. These results suggest that competitive interactions
 dominated the ungrazed community, while facilitative interactions
 dominated the grazed community. In sum, we successfully executed a new
 sampling method, using digital photography and a Geographical Information
 System, to collect experimental data on the spatial point patterns for the
 populations in this grassland community.
description: 1. Data collection using digital photographs and GIS
 A flat 5 m x 5 m sampling block was chosen in a study grassland
 community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm
 x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake
 and positioned in the center of each sub-block to photograph vegetation
 within a 0.25 m<sup>2 </sup>area. Pictures were taken 1.75 m
 above the ground at an approximate downward angle of 90° (Fig. 2).
 Automatic camera settings were used for focus, lighting and shutter speed.
 After photographing the plot as a whole, photographs were taken of each
 individual plant in each sub-block. In order to identify each individual
 plant from the digital images, each plant was uniquely marked before the
 pictures were taken (Fig. 2 B). Digital images were
 imported into a computer as JPEG files, and the position of each plant in
 the pictures was determined using GIS. This involved four steps: 1) A
 reference frame (Fig. 3) was established using R2V software to designate
 control points, or the four vertexes of each sub-block (Appendix S1), so
 that all plants in each sub-block were within the same reference frame.
 The parallax and optical distortion in the raster images was then
 geometrically corrected based on these selected control points; 2) Maps,
 or layers in GIS terminology, were set up for each species as PROJECT
 files (Appendix S2), and all individuals in each sub-block were digitized
 using R2V software (Appendix S3). For accuracy, the digitization of plant
 individual locations was performed manually; 3) Each plant species layer
 was exported from a PROJECT file to a SHAPE file in R2V software (Appendix
 S4); 4) Finally each species layer was opened in Arc GIS software in the
 SHAPE file format, and attribute data from each species layer was exported
 into Arc GIS to obtain the precise coordinates for each species. This last
 phase involved four steps of its own, from adding the data (Appendix S5),
 to opening the attribute table (Appendix S6), to adding new x and y
 coordinate fields (Appendix S7) and to obtaining the x and y coordinates
 and filling in the new fields (Appendix S8). 2. Data
 reliability assessment To determine the accuracy of
 our new method, we measured the individual locations of <i>Leymus
 chinensis</i>, a perennial rhizome grass, in representative
 community blocks 5 m x 5 m in size in typical steppe habitat in the Inner
 Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our
 standard for comparison, we used a ruler to measure the individual
 coordinates of <i>L. chinensis</i>. We tested for significant
 differences between (1) the coordinates of <i>L.
 chinensis</i>, as measured with our new method and with the ruler,
 and (2) the pair correlation function <i>g</i> of <i>L.
 chinensis</i>, as measured with our new method and with the ruler
 (see section 3.2 Data Analysis). If (1) the coordinates of <i>L.
 chinensis</i>, as measured with our new method and with the ruler,
 and (2) the pair correlation function <i>g</i> of <i>L.
 chinensis</i>, as measured with our new method and with the ruler,
 did not differ significantly, then we could conclude that our new method
 of measuring the coordinates of <i>L. chinensis</i> was
 reliable. We compared the results using a
 <i>t</i>-test (Table 1). We found no significant differences
 in either (1) the coordinates of <i>L. chinensis</i> or (2)
 the pair correlation function <i>g</i> of <i>L.
 chinensis</i>. Further, we compared the pattern characteristics of
 <i>L. chinensis</i> when measured by our new method against
 the ruler measurements using a null model. We found that the two pattern
 characteristics of <i>L. chinensis</i> did not differ
 significantly based on the homogenous Poisson process or complete spatial
 randomness (Fig. 4 B). Thus, we concluded that the data obtained using our
 new method was reliable enough to perform point pattern analysis with a
 null model in grassland communities.