variation partitioning environment space time rcode rscript

Variation partitioning environment space time rcode rscript series#

Although tree species diversity is low compared to that of other biomes, forest structural, successional, and landscape diversity are high under natural conditions ( Burton, 2013 Kuuluvainen and Siitonen, 2013). The boreal forest biome contains about one third of the global forest area and half of the remaining global near-natural frontier forests. The tree species- and size-related variation in the trees’ growing space suggests that among-tree competition, together with clustering of trees, shape the spatial assembly of the forest. Size- and species-dependent differences in local tree densities accumulated mostly at the scale of a few meters but showed also broader-scale variation possibly related to edaphic variation within the study plot. Trees close to each other were smaller than average. pubescens occupied on average 5–10 m 2 more space than shade-tolerant P. Depending on diameter class, shade-intolerant B. Mean space occupied by trees roughly doubled from the smallest diameter class (0–5 cm) to the largest (>25 cm), from 13.7 to 26.7 m 2. The GAM models showed that tree growing space increased non-linearly with tree size before saturating, and that overall growing space was larger for B. We used i-to-any L-functions to study the scale-dependence of tree densities around focal trees, and mark correlation functions to study the relative sizes of trees close to each other. We modeled the Voronoi polygon area as a function of tree size and species by using generalized additive models (GAM). We used Voronoi polygons to quantify the horizontal space potentially available to each tree. Specifically, we asked (1) how does growing space occupied by trees vary by tree species and size in an old-growth forest with open canopy structure, and (2) at what scales does the variation in tree growing space occur? We mapped an 8.8 ha forest plot with 4,884 live trees. We studied partitioning of horizontal space among trees and related spatial structures in an old-growth Picea abies (L.) Karst -dominated forest in northeastern subarctic Fennoscandia, where Betula pubescens (Ehrh.) is an important co-dominant. The distribution of space among forest trees is linked to the availability of resources, among-tree competition, and hence forest dynamics. 4School of Forest Sciences, University of Eastern Finland, Joensuu, Finland.3Biodiversity Centre, Finnish Environment Institute, Helsinki, Finland.2Department of Forest Sciences, University of Helsinki, Helsinki, Finland.1Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Lahti, Finland.do.Pasi Pouta 1*†, Niko Kulha 2,3†, Timo Kuuluvainen 2† and Tuomas Aakala 4† split by day, lapply the first function and rbind back together. Suppose you want only the first value of each day. You can split by day (or week, or month, etc), apply a function, then recombine split(xobj, 'days') #create a list where each element is the data for a different day

If you only want the data at the endpoints, you can use OHLC=FALSE to.daily(xobj, OHLC=FALSE)įor more basic subsetting, and much more, visit Īs mentions in the comments, split.xts is INCREDIBLY useful. Notice that the above creates columns for Open, High, Low, and Close.

# xobj.Open xobj.High xobj.Low xobj.Close

Or you can convert to a lower frequency to.weekly(xobj) You can use the endpoints function to find the rows that are the last rows of a time period ('hours', 'days', 'weeks', etc.) endpoints(xobj, 'days') If you want to get all data from any day that is between 19:00 and 20:00, xobj Or, if you want all data from October 2011, xobj You can also subset out time spans like this (all data between and including -25) xobj Xobj <- xts(dat, as.POSIXct(paste(dat, dat))) 01:00:00 11', header=FALSE, stringsAsFactors=FALSE)

Variation partitioning environment space time rcode rscript series#

I'd use a time series class such as xts dat <- read.table(text=' 01:00:00 12 If you wanted to sum within date: tapply(dat$obs, as.POSIXlt(dat$date)$yday, sum) The cut.POSIXt function adds divisions at other natural boundaries E.g. It's a list structure with these indices: 'yday', 'wday', 'year', 'mon', 'mday', 'hour', 'min', 'sec' and 'isdt'. The POSIXlt class does not work well inside dataframes but it can ve very handy for creating time based groups. # Notice these are not the same functions First I entered the data with the multiple spaces replaced with tabs.