Environmental conditions and habitats of GOS sites and bottle data of HOT-ALOHA station

The Global Ocean Sampling (GOS) expedition is currently the largest and geographically most comprehensive metagenomic dataset, including samples from the Atlantic, Pacific, and Indian Oceans. This study makes use of the wide range of environmental conditions and habitats encompassed within the GOS sites in order to investigate the ecological structuring of bacterial and archaeal taxon ranks. Community structures based on taxonomically classified 16S ribosomal RNA (rRNA) gene fragments at phylum, class, order, family, and genus rank levels were examined using multivariate statistical analysis, and the results were inspected in the context of oceanographic environmental variables and structured habitat classifications. At all taxon rank levels, community structures of neritic, oceanic, estuarine biomes, as well as other exotic biomes (salt marsh, lake, mangrove), were readily distinguishable from each other. A strong structuring of the communities with chlorophyll a concentration and a weaker yet significant structuring with temperature and salinity were observed. Furthermore, there were significant correlations between community structures and habitat classification. These results were used for further investigation of one-to-one relationships between taxa and environment and provided indications for ecological preferences shaped by primary production for both cultured and uncultured bacterial and archaeal clades.

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la din��mica de los procesos medio ambientales est�� considerado como un punto de gran inter��s en el campo medioambiental. La cobertura espacio temporal de los datos de teledetecci��n proporciona informaci��n continua con una alta frecuencia temporal, permitiendo el an��lisis de la evoluci��n de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetecci��n ha sido ampliamente probado, en la actualidad solo existe un n��mero reducido de metodolog��as que permiten su an��lisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetecci��n, basado en la combinaci��n del an��lisis estad��stico de series de tiempo y la fenometr��a. El objetivo principal es demostrar el uso de las series temporales de datos de teledetecci��n para analizar la din��mica de variables medio ambientales de una forma cuantitativa. Los objetivos espec��ficos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos emp��ricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos espec��ficos son: (1) evaluar y cartografiar estados fenol��gicos del cultivo del algod��n mediante an��lisis espectral y fenometr��a, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclim��ticas mediante modelos din��micos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos din��micos y (4) evaluar el funcionamiento de la vegetaci��n en base a la autocorrelaci��n temporal y la fenometr��a. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los ��ndices espectrales AS1 y AS2. Los par��metros fenol��gicos derivados del ajuste de funciones permiten la identificaci��n de distintos estados fenol��gicos del cultivo del algod��n. El an��lisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el ��ndice AS2 y de dos ciclos en el AS1 as�� como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterr��nea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la din��mica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelaci��n temporal para definir y caracterizar el funcionamiento de la vegetaci��n a nivel pixel. Finalmente el concepto ���Optical Functional Type��� ha sido definido, donde se propone que los pixeles deber��an ser considerados como unidades temporales y analizados en funci��n de su din��mica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the ���Optical Functional Types��� concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

A trait-based approach for predicting species responses to environmental change from sparse data : how well might terrestrial mammals track climate change?

Funded by COST (European Cooperation in Science and Technology) CEH projects. Grant Numbers: NEC05264, NEC05100 Natural Environment Research Council UK. Grant Number: NE/J008001/1 �� 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.