Manuel Delgado-Baquerizo

Bio: Manuel Delgado-Baquerizo is an academic researcher from Pablo de Olavide University. The author has contributed to research in topics: Ecosystem & Biodiversity. The author has an hindex of 50, co-authored 195 publications receiving 9586 citations. Previous affiliations of Manuel Delgado-Baquerizo include Cooperative Institute for Research in Environmental Sciences & King Juan Carlos University.

A global atlas of the dominant bacteria found in soil

Abstract: The immense diversity of soil bacterial communities has stymied efforts to characterize individual taxa and document their global distributions. We analyzed soils from 237 locations across six continents and found that only 2% of bacterial phylotypes (~500 phylotypes) consistently accounted for almost half of the soil bacterial communities worldwide. Despite the overwhelming diversity of bacterial communities, relatively few bacterial taxa are abundant in soils globally. We clustered these dominant taxa into ecological groups to build the first global atlas of soil bacterial taxa. Our study narrows down the immense number of bacterial taxa to a “most wanted” list that will be fruitful targets for genomic and cultivation-based efforts aimed at improving our understanding of soil microbes and their contributions to ecosystem functioning.

Microbial diversity drives multifunctionality in terrestrial ecosystems

Abstract: Despite the importance of microbial communities for ecosystem services and human welfare, the relationship between microbial diversity and multiple ecosystem functions and services (that is, multifunctionality) at the global scale has yet to be evaluated. Here we use two independent, large-scale databases with contrasting geographic coverage (from 78 global drylands and from 179 locations across Scotland, respectively), and report that soil microbial diversity positively relates to multifunctionality in terrestrial ecosystems. The direct positive effects of microbial diversity were maintained even when accounting simultaneously for multiple multifunctionality drivers (climate, soil abiotic factors and spatial predictors). Our findings provide empirical evidence that any loss in microbial diversity will likely reduce multifunctionality, negatively impacting the provision of services such as climate regulation, soil fertility and food and fibre production by terrestrial ecosystems.

Plant Species Richness and Ecosystem Multifunctionality in Global Drylands

Abstract: Experiments suggest that biodiversity enhances the ability of ecosystems to maintain multiple functions, such as carbon storage, productivity, and the buildup of nutrient pools (multifunctionality). However, the relationship between biodiversity and multifunctionality has never been assessed globally in natural ecosystems. We report here on a global empirical study relating plant species richness and abiotic factors to multifunctionality in drylands, which collectively cover 41% of Earth’s land surface and support over 38% of the human population. Multifunctionality was positively and significantly related to species richness. The best-fitting models accounted for over 55% of the variation in multifunctionality and always included species richness as a predictor variable. Our results suggest that the preservation of plant biodiversity is crucial to buffer negative effects of climate change and desertification in drylands.

Decoupling of soil nutrient cycles as a function of aridity in global drylands

Abstract: The biogeochemical cycles of carbon (C), nitrogen (N) and phosphorus (P) are interlinked by primary production, respiration and decomposition in terrestrial ecosystems. It has been suggested that the C, N and P cycles could become uncoupled under rapid climate change because of the different degrees of control exerted on the supply of these elements by biological and geochemical processes. Climatic controls on biogeochemical cycles are particularly relevant in arid, semi-arid and dry sub-humid ecosystems (drylands) because their biological activity is mainly driven by water availability. The increase in aridity predicted for the twenty-first century in many drylands worldwide may therefore threaten the balance between these cycles, differentially affecting the availability of essential nutrients. Here we evaluate how aridity affects the balance between C, N and P in soils collected from 224 dryland sites from all continents except Antarctica. We find a negative effect of aridity on the concentration of soil organic C and total N, but a positive effect on the concentration of inorganic P. Aridity is negatively related to plant cover, which may favour the dominance of physical processes such as rock weathering, a major source of P to ecosystems, over biological processes that provide more C and N, such as litter decomposition. Our findings suggest that any predicted increase in aridity with climate change will probably reduce the concentrations of N and C in global drylands, but increase that of P. These changes would uncouple the C, N and P cycles in drylands and could negatively affect the provision of key services provided by these ecosystems.

Supporting Online Material for Plant Species Richness and Ecosystem Multifunctionality in Global Drylands

Abstract: Fernando T. Maestre,* Jose L. Quero, Nicholas J. Gotelli, Adrian Escudero, Victoria Ochoa, Manuel Delgado-Baquerizo, Miguel Garcia-Gomez, Matthew A. Bowker, Santiago Soliveres, Cristina Escolar, Pablo Garcia-Palacios, Miguel Berdugo, Enrique Valencia, Beatriz Gozalo, Antonio Gallardo, Lorgio Aguilera, Tulio Arredondo, Julio Blones, Bertrand Boeken, Donaldo Bran, Abel A. Conceicao, Omar Cabrera, Mohamed Chaieb, Mchich Derak, David J. Eldridge, Carlos I. Espinosa, Adriana Florentino, Juan Gaitan, M. Gabriel Gatica, Wahida Ghiloufi, Susana Gomez-Gonzalez, Julio R. Gutierrez, Rosa M. Hernandez, Xuewen Huang, Elisabeth Huber-Sannwald, Mohammad Jankju, Maria Miriti, Jorge Monerris, Rebecca L. Mau, Ernesto Morici, Kamal Naseri, Abelardo Ospina, Vicente Polo, Anibal Prina, Eduardo Pucheta, David A. Ramirez-Collantes, Roberto Romao, Matthew Tighe, Cristian Torres-Diaz, James Val, Jose P. Veiga, Deli Wang, Eli Zaady

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Biodiversity loss and its impact on humanity

Abstract: The most unique feature of Earth is the existence of life, and the most extraordinary feature of life is its diversity. Approximately 9 million types of plants, animals, protists and fungi inhabit the Earth. So, too, do 7 billion people. Two decades ago, at the first Earth Summit, the vast majority of the world's nations declared that human actions were dismantling the Earth's ecosystems, eliminating genes, species and biological traits at an alarming rate. This observation led to the question of how such loss of biological diversity will alter the functioning of ecosystems and their ability to provide society with the goods and services needed to prosper.

Metagenomic biomarker discovery and explanation

Abstract: This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.