Sensitivity of codispersion to noise and error in ecological and environmental data

Codispersion analysis is a new statistical method developed to assess spatial covariation between two spatial processes that may not be isotropic or stationary. Its application to anisotropic ecological datasets have provided new insights into mechanisms underlying observed patterns of species distributions and the relationship between individual species and underlying environmental gradients. However, the performance of the codispersion coefficient when there is noise or measurement error ("contamination") in the data has been addressed only theoretically. Here, we use Monte Carlo simulations and real datasets to investigate the sensitivity of codispersion to four types of contamination commonly seen in many real-world environmental and ecological studies. Three of these involved examining codispersion of a spatial dataset with a contaminated version of itself. The fourth examined differences in codisperson between plants and soil conditions, where the estimates of soil characteristics were based on complete or thinned datasets. In all cases, we found that estimates of codispersion were robust when contamination, such as data thinning, was relatively low (<15\%), but were sensitive to larger percentages of contamination. We also present a useful method for imputing missing spatial data and discuss several aspects of the codispersion coefficient when applied to noisy data to gain more insight about the performance of codispersion in practice.Comment: 20 pages, 14 figure

Análisis de la planificación de entretenimiento deportivo de los entrenadores y aspectos de integración social en niños de categorías menores de futbol del Juventus FC, Agosto-Diciembre 2022

La presente investigación con el tema " Análisis de la planificación de entrenamiento deportivo de los entrenadores de categorías menores de futbol de Juventus FC , Agosto-Diciembre 2022" Pretende darle solución a la problemática de planificación del entrenamiento deportivo en las categorías menores de la institución durante el periodo preparatoria o de pretemporada, mediante una propuesta de una estructura de planificación de un meso ciclo entrante y básico desarrollador del entretenimient

Detecting Ecological Patterns Along Environmental Gradients: Alpine Treeline Ecotones

Everyone is familiar with that age-old adage: "a picture is worth a thousand words". Among ecologists, the word "picture" easily could be replaced with the word "pattern", although the significance remains the same: the pattern we observe in a single snapshot more than sums up what could be expressed if we tried to describe all the original events that led to the pattern. One particular class of patterns, spatial patterns, are the backbone of much contemporary ecological research. [...

Higher site productivity and stand age enhance forest susceptibility to drought-induced mortality

Warmer and drier conditions increase forest mortality worldwide. At the same time, nitrogen deposition, longer growing seasons and higher atmospheric $CO_{2}$ concentrations may increase site productivity accelerating forest growth. However, tree physiological studies suggest that increased site productivity can also have adverse effects, reducing adaptation to drought. Understanding such intricate interactions that might foster tree mortality is essential for designing activities and policies aimed at preserving forests and the ecosystem services they provide. This study shows how site factors and stand features affect the susceptibility of Scots pine to drought-induced stand-level mortality. We use extensive forest data covering 750,000 ha, including 47,450 managed Scots pine stands, of which 2,547 were affected by mortality during the drought in 2015-2019. We found that the oldest and most dense stands growing on the most productive sites showed the highest susceptibility to enhanced mortality during drought. Our findings suggest that increasing site productivity may accelerate the intensity and prevalence of drought-induced forest mortality. Therefore, climate change may increase mortality, particularly in old and high-productive forests. Such exacerbated susceptibility to mortality should be considered in forest carbon sink projections, forest management, and policies designed to increase resilience and protect forest ecosystems

Assessing the association between two spatial or temporal sequences

This paper deals with the codispersion coefficient for spatial and temporal series. We present some results and simulations concerning the codispersion coefficient in the context of spatial models. The results obtained are immediate consequences of the asymptotic normality of the sample codispersion coefficient and show certain limitations of the coefficient. New simulation studies provide information about the performance of the coefficient with respect to other coefficients of spatial association. The behavior of the codispersion coefficient under additively contaminated processes is also studied via Monte Carlo simulations. In the context of time series, explicit expressions for the asymptotic variance of the sample version of the coefficient are given for autoregressive and moving average processes. Resampling methods are used to compute the variance of the coefficient. A real data example is presented to explore how well the codispersion coefficient captures the comovement between two time series in practice.spatial association, autoregressive models, correlation coefficient, codispersion coefficient, time series,

Spatial relationships between two georeferenced variables: with applications in R

This book offers essential, systematic information on the assessment of the spatial association between two processes from a statistical standpoint. Divided into eight chapters, the book begins with preliminary concepts, mainly concerning spatial statistics. The following seven chapters focus on the methodologies needed to assess the correlation between two or more processes; from theory introduced 35 years ago, to techniques that have only recently been published. Furthermore, each chapter contains a section on R computations to explore how the methodology works with real data. References and a list of exercises are included at the end of each chapter. The assessment of the correlation between two spatial processes has been tackled from several different perspectives in a variety of applications fields. In particular, the problem of testing for the existence of spatial association between two georeferenced variables is relevant for posterior modeling and inference. One evident application in this context is the quantification of the spatial correlation between two images (processes defined on a rectangular grid in a two-dimensional space). From a statistical perspective, this problem can be handled via hypothesis testing, or by using extensions of the correlation coefficient. In an image-processing framework, these extensions can also be used to define similarity indices between images.

Codispersion coefficients for spatial and temporal series

The paper gives explicit formulas for the first moments of a sample codispersion coefficient defined as a suitably normalized sum of products of increments for time or space sequences. Derived formulas allow for the optimal choice of the lag in several spatial and temporal models and lead to tests of independence and confidence intervals for correlation.