 Recording is on. Hello, my name is Adriana. Today I want to tell you about the structural connectivity in the Lower Uruguay River Forest, a use of Earth for landscape ecology. I do this work together with Maria Vasallo and Natalia Brandeira. We want to measure the structural connectivity of the landscape to know the state conservation in the study area. This information is useful to face the challenge in the conservation of the biodiversity that the area presents, particularly in the Isidro-Philip Forest and the Open Forest. The story area is located between two countries, half in Argentina and the other in Uruguay. And covers an area of approximately 4,769,200 hectares. We estimate the structural connectivity of the Isidro-Philip Forest and the Open Forest in the advising of the main distributor of the Lower Uruguay River for this three-year. Where inputs were lens cover classification previously generated with unsupervised classification and lensed image. The study area was subdivided into cells of one square kilometer. This is approximately 50,000 cells in the interior area. The connectivity was estimated by calculating 14 landscape metrics for each of the cells and for each of the dates. We perform a principal component analysis, PCA for sure, to reduce the dimensionality of landscape connectivity metrics and have an easier measure. We call retin-marlin. This is a large amount of data to process in the use software. So with the C to use error. These are delivery we use. We library SF, a support for a simple filter and reading and writing him data and for performance on hermetical operation. Tidy birth and DPLAR. It's a low work with data table and data frame. This state contains function for a statistical calculation and this being for Excel and simplify the creation of Excel. Factor extra or factor minimum is you multivariate the explorative data analysis. Data minimum and we solicit visualization of multivariate analysis output. This code for PCA. Here import the file with connectivity index result. Later delete the NAs in the data frame. And later performs principal component analysis. Finally, it's the information for a balance and a vector and information for observation. Here we can see output of PCA. And N values are N vectors for the result of PCA. We reduce the dimensional of 2 because dimension 1 and dimension 2 explains 70% of the variability. We define the greatest connectivity is those of observation that are the second pattern. Here because there they fill the high connectivity values for the index. How when we want to see connectivity spread especially we generate a shape to be able to make a map. Third import the file result of PCA and layer convert CSV file in the shape file. Finally generate a shape file with the connectivity index and the coordinate of the observation. Here are two examples of connectivity map. This is map for open forest. This is map for air flick forest. The connectivity differ along the Uruguay River and the craze as we move away from the Uruguay River. The connectivity pattern observe differ between the two types of forest. The use of air and allow use to generate a simple measure connectivity with the analysis of PCA. And also to be able to obtain shape file for the connectivity maps and visualize an analysis special pattern of connectivity. Thank you for your attention.