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Florian Jeltsch

Bio: Florian Jeltsch is an academic researcher from University of Potsdam. The author has contributed to research in topics: Population & Climate change. The author has an hindex of 49, co-authored 203 publications receiving 12294 citations. Previous affiliations of Florian Jeltsch include Helmholtz Centre for Environmental Research - UFZ & Free University of Berlin.


Papers
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Journal ArticleDOI
TL;DR: It is shown that ecological effects of habitat heterogeneity may vary considerably between species groups depending on whether structural attributes are perceived as heterogeneity or fragmentation, and possible effects may also vary relative to the structural variable measured.
Abstract: Aim In a selected literature survey we reviewed studies on the habitat heterogeneity–animal species diversity relationship and evaluated whether there are uncertainties and biases in its empirical support. Location World-wide. Methods We reviewed 85 publications for the period 1960–2003. We screened each publication for terms that were used to define habitat heterogeneity, the animal species group and ecosystem studied, the definition of the structural variable, the measurement of vegetation structure and the temporal and spatial scale of the study. Main conclusions The majority of studies found a positive correlation between habitat heterogeneity/diversity and animal species diversity. However, empirical support for this relationship is drastically biased towards studies of vertebrates and habitats under anthropogenic influence. In this paper, we show that ecological effects of habitat heterogeneity may vary considerably between species groups depending on whether structural attributes are perceived as heterogeneity or fragmentation. Possible effects may also vary relative to the structural variable measured. Based upon this, we introduce a classification framework that may be used for across-studies comparisons. Moreover, the effect of habitat heterogeneity for one species group may differ in relation to the spatial scale. In several studies, however, different species groups are closely linked to ‘keystone structures’ that determine animal species diversity by their presence. Detecting crucial keystone structures of the vegetation has profound implications for nature conservation and biodiversity management.

2,668 citations

Journal ArticleDOI
11 Nov 2005-Science
TL;DR: This paper argues that recent advances in ecological modeling have come together in a general strategy that provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.
Abstract: Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.

1,933 citations

Journal ArticleDOI
Marlee A. Tucker1, Katrin Böhning-Gaese1, William F. Fagan2, John M. Fryxell3, Bram Van Moorter, Susan C. Alberts4, Abdullahi H. Ali, Andrew M. Allen5, Andrew M. Allen6, Nina Attias7, Tal Avgar8, Hattie L. A. Bartlam-Brooks9, Buuveibaatar Bayarbaatar10, Jerrold L. Belant11, Alessandra Bertassoni12, Dean E. Beyer13, Laura R. Bidner14, Floris M. van Beest15, Stephen Blake10, Stephen Blake16, Niels Blaum17, Chloe Bracis1, Danielle D. Brown18, P J Nico de Bruyn19, Francesca Cagnacci20, Francesca Cagnacci21, Justin M. Calabrese22, Justin M. Calabrese2, Constança Camilo-Alves23, Simon Chamaillé-Jammes24, André Chiaradia25, André Chiaradia26, Sarah C. Davidson16, Sarah C. Davidson27, Todd E. Dennis28, Stephen DeStefano29, Duane R. Diefenbach30, Iain Douglas-Hamilton31, Iain Douglas-Hamilton32, Julian Fennessy, Claudia Fichtel33, Wolfgang Fiedler16, Christina Fischer34, Ilya R. Fischhoff35, Christen H. Fleming2, Christen H. Fleming22, Adam T. Ford36, Susanne A. Fritz1, Benedikt Gehr37, Jacob R. Goheen38, Eliezer Gurarie39, Eliezer Gurarie2, Mark Hebblewhite40, Marco Heurich41, Marco Heurich42, A. J. Mark Hewison43, Christian Hof, Edward Hurme2, Lynne A. Isbell14, René Janssen, Florian Jeltsch17, Petra Kaczensky44, Adam Kane45, Peter M. Kappeler33, Matthew J. Kauffman38, Roland Kays46, Roland Kays47, Duncan M. Kimuyu48, Flávia Koch49, Flávia Koch33, Bart Kranstauber37, Scott D. LaPoint16, Scott D. LaPoint50, Peter Leimgruber22, John D. C. Linnell, Pascual López-López51, A. Catherine Markham52, Jenny Mattisson, Emília Patrícia Medici53, Ugo Mellone54, Evelyn H. Merrill8, Guilherme Miranda de Mourão55, Ronaldo Gonçalves Morato, Nicolas Morellet43, Thomas A. Morrison56, Samuel L. Díaz-Muñoz57, Samuel L. Díaz-Muñoz14, Atle Mysterud58, Dejid Nandintsetseg1, Ran Nathan59, Aidin Niamir, John Odden, Robert B. O'Hara60, Luiz Gustavo R. Oliveira-Santos7, Kirk A. Olson10, Bruce D. Patterson61, Rogério Cunha de Paula, Luca Pedrotti, Björn Reineking62, Björn Reineking63, Martin Rimmler, Tracey L. Rogers64, Christer Moe Rolandsen, Christopher S. Rosenberry65, Daniel I. Rubenstein66, Kamran Safi67, Kamran Safi16, Sonia Saïd, Nir Sapir68, Hall Sawyer, Niels Martin Schmidt15, Nuria Selva69, Agnieszka Sergiel69, Enkhtuvshin Shiilegdamba10, João P. Silva70, João P. Silva71, João P. Silva72, Navinder J. Singh5, Erling Johan Solberg, Orr Spiegel14, Olav Strand, Siva R. Sundaresan, Wiebke Ullmann17, Ulrich Voigt44, Jake Wall31, David W. Wattles29, Martin Wikelski67, Martin Wikelski16, Christopher C. Wilmers73, John W. Wilson74, George Wittemyer31, George Wittemyer75, Filip Zięba, Tomasz Zwijacz-Kozica, Thomas Mueller1, Thomas Mueller22 
Goethe University Frankfurt1, University of Maryland, College Park2, University of Guelph3, Duke University4, Swedish University of Agricultural Sciences5, Radboud University Nijmegen6, Federal University of Mato Grosso do Sul7, University of Alberta8, Royal Veterinary College9, Wildlife Conservation Society10, Mississippi State University11, Sao Paulo State University12, Michigan Department of Natural Resources13, University of California, Davis14, Aarhus University15, Max Planck Society16, University of Potsdam17, Middle Tennessee State University18, Mammal Research Institute19, Harvard University20, Edmund Mach Foundation21, Smithsonian Conservation Biology Institute22, University of Évora23, University of Montpellier24, Parks Victoria25, Monash University26, Ohio State University27, Fiji National University28, University of Massachusetts Amherst29, United States Geological Survey30, Save the Elephants31, University of Oxford32, German Primate Center33, Technische Universität München34, Institute of Ecosystem Studies35, University of British Columbia36, University of Zurich37, University of Wyoming38, University of Washington39, University of Montana40, University of Freiburg41, Bavarian Forest National Park42, University of Toulouse43, University of Veterinary Medicine Vienna44, University College Cork45, North Carolina State University46, North Carolina Museum of Natural Sciences47, Karatina University48, University of Lethbridge49, Lamont–Doherty Earth Observatory50, University of Valencia51, Stony Brook University52, International Union for Conservation of Nature and Natural Resources53, University of Alicante54, Empresa Brasileira de Pesquisa Agropecuária55, University of Glasgow56, New York University57, University of Oslo58, Hebrew University of Jerusalem59, Norwegian University of Science and Technology60, Field Museum of Natural History61, University of Grenoble62, University of Bayreuth63, University of New South Wales64, Pennsylvania Game Commission65, Princeton University66, University of Konstanz67, University of Haifa68, Polish Academy of Sciences69, University of Lisbon70, University of Porto71, Instituto Superior de Agronomia72, University of California, Santa Cruz73, University of Pretoria74, Colorado State University75
26 Jan 2018-Science
TL;DR: Using a unique GPS-tracking database of 803 individuals across 57 species, it is found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in area with a low human footprint.
Abstract: Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.

719 citations

Journal ArticleDOI
01 Feb 2003-Oikos
TL;DR: Using the pattern-oriented approach to its full potential will require a major paradigm shift in the strategies of modeling and data collection, and it is argued that more emphasis must be placed on observing and documenting relevant patterns in addition to attempts to obtain direct estimates of model parameters.
Abstract: We suggest that the conscious use of information that is “hidden” in distinct structures in nature itself and in data extracted from nature (=pattern) during the process of modeling (=pattern-oriented modeling) can substantially improve models in ecological application and conservation Observed patterns, such as time-series patterns and spatial patterns of presence/absence in habitat patches, contain a great deal of data on scales, site-history, parameters and processes Use of these data provides criteria for aggregating the biological information in the model, relates the model explicitly to the relevant scales of the system, facilitates the use of helpful techniques of indirect parameter estimation with independent data, and helps detect underlying ecological processes Additionally, pattern-oriented models produce comparative predictions that can be tested in the field We developed a step-by-step protocol for pattern-oriented modeling and illustrate the potential of this protocol by discussing three pattern-oriented population models: (1) a population viability analysis for brown bears (Ursusarctos) in northern Spain using time-series data on females with cubs of the year to adjust unknown model parameters; (2) a savanna model for detecting underlying ecological processes from spatial patterns of tree distribution; and (3) the incidence function model of metapopulation dynamics as an example of process integration and model generalization We conclude that using the pattern-oriented approach to its full potential will require a major paradigm shift in the strategies of modeling and data collection, and we argue that more emphasis must be placed on observing and documenting relevant patterns in addition to attempts to obtain direct estimates of model parameters

345 citations

Journal ArticleDOI
TL;DR: In this paper, the authors propose a new unifying concept of savanna existence, i.e., the concept of ecological buffering mechanisms, which integrates both earlier concepts of ecological theory and general ideas on savanna dynamics as well as specific studies of savannas in different parts of the world.
Abstract: Despite the large spatial extent and the obvious importance of the savanna biome, not to mention several decades of savanna research, the origin, age, nature, and dynamics of savannas are not well understood. Basically, the question surrounding the presence or existence of savannas focuses on the long-term coexistence of the dominating life forms – trees and grasses. How do these two very different components coexist, without one of them dominating the other, and what mechanisms determine the proportion of each? Earlier equilibrium concepts have recently been replaced by non-equilibrium concepts, and the current view is that tree-grass interactions in savannas cannot be predicted by a simple model. Instead, many interacting factors operating at various spatial and temporal scales contribute to creating and maintaining savanna physiognomy. In this paper we analyse a number of studies from savannas in different parts of the world and discuss whether a general pattern can be perceived behind the numerous factors influencing the presence of savannas systems. On the basis of this analysis we propose a new unifying concept of savanna existence, i.e., the concept of ecological buffering mechanisms. In contrast to previous approaches to explain tree-grass coexistence in savannas, the concept of buffering mechanisms does not focus on equilibria or non-equilibria, steady states of the system or domains of attraction. Instead, in the concept of ecological buffering mechanisms we suggest that it is much more useful to focus on the boundaries of savanna existence itself and to investigate the mechanisms that allow a savanna to persist in critical situations where this system is driven to its boundaries, e.g., pure grasslands or tropical forests. The concept of ecological buffering mechanisms integrates both earlier concepts of ecological theory and general ideas on savanna dynamics as well as specific studies of savannas in different parts of the world.

316 citations


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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
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

14,171 citations

Journal ArticleDOI

6,278 citations

01 Jan 2016
TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
Abstract: Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.

5,249 citations