Stability subtypes of callous–unemotional traits and conduct disorder symptoms and their correlates

Callous unemotional traits and conduct disorder symptoms tend to co-occur across development, with existing evidence pointing to individual differences in the co-development of these problems. The current study identified groups of at risk adolescents showing stable (i.e., high on both conduct disorder and callous-unemotional symptoms, high only on either callous-unemotional or conduct disorder symptoms) or increasing conduct disorder and callous-unemotional symptoms. Data were collected from a sample of 2038 community adolescents between 15 and 18 years (1070 females, Mage = 16) of age. A longitudinal design was followed in that adolescent reports were collected at two time points, one year apart. Increases in conduct disorder symptoms and callous-unemotional traits were accompanied by increases in anxiety, depressive symptoms, narcissism, proactive and reactive aggression and decreases in self-esteem. Furthermore, adolescents with high and stable conduct disorder symptoms and callous-unemotional traits were consistently at high risk for individual, behavioral and contextual problems. In contrast, youth high on callous-unemotional traits without conduct disorder symptoms remained at low-risk for anxiety, depressive symptoms, narcissism, and aggression, pointing to a potential protective function of pure callous-unemotional traits against the development of psychopathological problems

Measuring β-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allows information to be gathered over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (β‐diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript, we propose novel techniques to measure β‐diversity from airborne or satellite remote sensing, mainly based on: (1) multivariate statistical analysis, (2) the spectral species concept, (3) self‐organizing feature maps, (4) multidimensional distance matrices, and the (5) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating β‐diversity from remotely sensed imagery and potentially relating them to species diversity in the field

Monitoring ecological change during rapid socio-economic and political transitions: Colombian ecosystems in the post-conflict era

After more than 50-years of armed conflict, Colombia is now transitioning to a more stable social and political climate due to a series of peace agreements between the government and different armed groups. Consequences of these socio-economic and political changes on ecosystems are largely uncertain, but there is growing concern about derived increases in environmental degradation. Here, we review the capacity of Colombia to monitor the state of its ecosystems and their rate of change over time. We found several important programs currently set in place by different institutions as well as by independent groups of scientists that address different aspects of environmental monitoring. However, most of the current initiatives could be improved in terms of data coverage, quality and access, and could be better articulated among each other. We propose a set of activities that would increase the capacity of Colombia to monitor its ecosystems, provide useful information to policy makers, and facilitate scientific research. These include: 1) the establishment of a national center for ecological synthesis that focuses on analyzing existing information; 2) the establishment of an ecological observatory system that collects new information, integrates remote sensing products, and produces near real-time products on key ecological variables; and 3) the creation of new platforms for dialog and action within existing scientific and policy groups

A Range of Earth Observation Techniques for Assessing Plant Diversity

AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Modeling species distribution using niche-based proxies derived from composite bioclimatic variables and MODIS NDVI

Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species