![]() Thinking Questions 1) What data will you need to collect to determine the value for "G"? Since the formula for force of gravity is F=G m1m2/r2 I will need to figure out the F, m1, m2, andr 2) How will you put this information into a data table that Excel can help you graph and find the best fit line? I will graph it 3) Write your procedure here "Remember a good procedure is a series of steps that someone else could executeĬonduct your investigation recording your data in Excel. You will develop your own procedure, collect data, graph your data, find a best fit line and interpret its slope to find G. ![]() # 6 463.2 6.03 7.6 0.254 0.000 0.Part 2 - Quantitative Measurements In this section of the lab, you will develop your own method for determining the gravitational constant G in the formula for gravity using the simulation and Excel. # Perim Depth pH Dforest Drock Dshrub geometry # SiteName betweenness degree closeness SiteID Elev Length Area ![]() # Attribute-geometry relationship: 14 constant, 0 aggregate, 0 identity Sites <- st_as_sf(sites, coords = c( "X", "Y"), crs = prj, agr = "constant") head(sites) # Simple feature collection with 6 features and 14 fields # ID RALU SiteName geometry betweenness degree # Attribute-geometry relationship: 3 constant, 0 aggregate, 0 identity, 3 NA's Wetlands $betweenness <- igraph :: betweenness(wg, directed= FALSE, weights=w) wetlands $degree <- igraph :: degree(wg) wetlands $closeness <- igraph :: closeness(wg, weights=w) wetlands # Simple feature collection with 121 features and 6 fields Linking paternity to ecological variables Nested model (NMLPE) for hierarchical sampling designs A quick note on controlling for population structure in RDA Redundancy Analysis (RDA): a multivariate GEA Latent Factor Mixed Models (LFMM): a univariate GEA How does changing resolution affect these metrics? Convert conductance into effective distance Setting cost values and calculating conductance Simulated data: 2-island model with admixture Benchmarking file import and export options Run simulator using a previously defined parameter set Calculate Hanski’s index Si with source patch parameters Fit spatially varying coefficients model with package ‘spmoran’ Spatial filtering with MEM using package ‘spmoran’ Fit spatial simultaneous autoregressive error models (SAR) Fit models with spatially correlated error (GLS) with package ‘nlme’ Test regression residuals for spatial autocorrelation Assess correlation between trait and environment Estimate genetic and non-genetic variance components from a common garden experiment Specify spatial weights and calculate Moran’s I Create Mantel correlogram for genetic data Are genetic differentiation and diversity related? What determines genetic differentiation among sites? Spatial distribution of genetic structure Aggregate genetic data at population level (allele frequencies) Basic checking of markers and populations Use ‘terra’ and ‘tmap’ to display categorical map with color scheme ![]() Convert ‘SpatialPointsDataFrame’ to ‘sf’ object Sample landscape metrics within buffer around sampling locations Display raster data and overlay sampling locations, extract data View information stored in ‘genind’ object ![]()
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