Neutral Theory and the Evolution of Ecological Equivalence
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Functional Ecology's Non-Selectionist Understanding of Function
Studies in History and Philosophy of Biol & Biomed Sci xxx (xxxx) xxx–xxx Contents lists available at ScienceDirect Studies in History and Philosophy of Biol & Biomed Sci journal homepage: www.elsevier.com/locate/shpsc Functional ecology's non-selectionist understanding of function ∗ Antoine C. Dussaulta,b, a Collège Lionel-Groulx, 100, Rue Duquet, Sainte-Thérèse, Québec, J7E 3G6, Canada b Centre Interuniversitaire de Recherche sur la Science et la Technologie (CIRST), Université du Québec à Montréal, C.P. 8888, Succ. Centre-ville, Montréal, Québec, H3C 3P8, Canada ARTICLE INFO ABSTRACT Keywords: This paper reinforces the current consensus against the applicability of the selected effect theory of function in Functional biodiversity ecology. It does so by presenting an argument which, in contrast with the usual argument invoked in support of Function this consensus, is not based on claims about whether ecosystems are customary units of natural selection. Biodiversity Instead, the argument developed here is based on observations about the use of the function concept in func- Ecosystem function tional ecology, and more specifically, research into the relationship between biodiversity and ecosystem func- Biological individuality tioning. It is argued that a selected effect account of ecological functions is made implausible by the fact that it Superorganism would conflict with important aspects of the understanding of function and ecosystem functional organization which underpins functional ecology's research program. Specifically, it would conflict with (1) Functional ecology's adoption of a context-based understanding of function and its aim to study the functional equivalence between phylogenetically-divergent organisms; (2) Functional ecology's attribution to ecosystems of a lower degree of part-whole integration than the one found in paradigm individual organisms; and (3) Functional ecology's adoption of a physiological or metabolic perspective on ecosystems rather than an evolutionary one. -
Competitive Exclusion Principle
Competitive exclusion principle In ecology, the competitive exclusion principle,[1] sometimes referred to as Gause's law,[2] is a proposition named for Georgy Gause that two species competing for the same limited resource cannot coexist at constant population values. When one species has even the slightest advantage over another, the one with the advantage will dominate in the long term. This leads either to the extinction of the weaker competitor or to an evolutionary or behavioral shift toward a different ecological niche. The principle has been paraphrased in the maxim "complete competitors can not coexist".[1] 1: A smaller (yellow) species of bird forages Contents across the whole tree. 2: A larger (red) species competes for resources. History 3: Red dominates in the middle for the more abundant resources. Yellow adapts to a new Experimental basis niche restricted to the top and bottom of the tree, Prediction avoiding competition. Paradoxical traits Redefinition Phylogenetic context Application to humans See also References History The competitive exclusion principle is classically attributed to Georgii Gause,[3] although he actually never formulated it.[1] The principle is already present in Darwin's theory of natural selection.[2][4] Throughout its history, the status of the principle has oscillated between a priori ('two species coexisting must have different niches') and experimental truth ('we find that species coexisting do have different niches').[2] Experimental basis Based on field observations, Joseph Grinnell formulated the principle of competitive exclusion in 1904: "Two species of approximately the same food habits are not likely to remain long evenly balanced in numbers in the same region. -
Energy Flow Through Marine Ecosystems: Confronting Transfer Efficiency
TREE 2756 No. of Pages 11 Trends in Ecology & Evolution Review Energy Flow Through Marine Ecosystems: Confronting Transfer Efficiency Tyler D. Eddy ,1,2,*,@ Joey R. Bernhardt ,3,4,5,@ Julia L. Blanchard,6,7 William W.L. Cheung,3 Mathieu Colléter,8 Hubert du Pontavice ,3,9 Elizabeth A. Fulton,6,10 Didier Gascuel ,9,@ Kelly A. Kearney ,11 Colleen M. Petrik ,12 Tilla Roy,13,14 Ryan R. Rykaczewski ,1,15 Rebecca Selden,16 Charles A. Stock ,17 Colette C.C. Wabnitz ,3,18,19 and Reg A. Watson 6,7 Transfer efficiency is the proportion of energy passed between nodes in food webs. Highlights It is an emergent, unitless property that is difficult to measure, and responds Transfer efficiency is a key parameter de- dynamically to environmental and ecosystem changes. Because the consequences scribing ecosystem structure and func- of changes in transfer efficiency compound through ecosystems, slight variations tion and is used to estimate fisheries production; however, it is also one of can have large effects on food availability for top predators. Here, we review the the most uncertain parameters. processes controlling transfer efficiency, approaches to estimate it, and known variations across ocean biomes. Both process-level analysis and observed macro- Questions remain about how habitats, fi scale variations suggest that ecosystem-scale transfer efficiency is highly variable, food resources, shing pressure, spatio- fi temporal scales, as well as temperature, impacted by shing, and will decline with climate change. It is important that we primary production, and other climate more fully resolve the processes controlling transfer efficiency in models to drivers impact transfer efficiency. -
Meta-Ecosystems: a Theoretical Framework for a Spatial Ecosystem Ecology
Ecology Letters, (2003) 6: 673–679 doi: 10.1046/j.1461-0248.2003.00483.x IDEAS AND PERSPECTIVES Meta-ecosystems: a theoretical framework for a spatial ecosystem ecology Abstract Michel Loreau1*, Nicolas This contribution proposes the meta-ecosystem concept as a natural extension of the Mouquet2,4 and Robert D. Holt3 metapopulation and metacommunity concepts. A meta-ecosystem is defined as a set of 1Laboratoire d’Ecologie, UMR ecosystems connected by spatial flows of energy, materials and organisms across 7625, Ecole Normale Supe´rieure, ecosystem boundaries. This concept provides a powerful theoretical tool to understand 46 rue d’Ulm, F–75230 Paris the emergent properties that arise from spatial coupling of local ecosystems, such as Cedex 05, France global source–sink constraints, diversity–productivity patterns, stabilization of ecosystem 2Department of Biological processes and indirect interactions at landscape or regional scales. The meta-ecosystem Science and School of perspective thereby has the potential to integrate the perspectives of community and Computational Science and Information Technology, Florida landscape ecology, to provide novel fundamental insights into the dynamics and State University, Tallahassee, FL functioning of ecosystems from local to global scales, and to increase our ability to 32306-1100, USA predict the consequences of land-use changes on biodiversity and the provision of 3Department of Zoology, ecosystem services to human societies. University of Florida, 111 Bartram Hall, Gainesville, FL Keywords 32611-8525, -
The New Talent Landscape: Recruiting Difficulty and Skills Shortages (SHRM, 2016)
THE NEW TALENT LANDSCAPE RECRUITING DIFFICULTY AND SKILLS SHORTAGES THE NEW TALENT LANDSCAPE Recruiting Difficulty and Skills Shortages A RESEARCH REPORT BY THE SOCIETY FOR HUMAN RESOURCE MANAGEMENT Media Contact USA Kate Kennedy SHRM Headquarters Phone: + 1.703.535.6260 Alexandria, VA 22314 The Society for Human Resource Management E-mail: [email protected] Phone +1.800.283.7476 (SHRM) is the world’s largest HR professional society, E-mail [email protected] representing 285,000 members in more than 165 Vanessa Hill countries. For nearly seven decades, the Society Phone: +1.703.535.6072 China has been the leading provider of resources serving E-mail: [email protected] Gateway Plaza the needs of HR professionals and advancing the Chaoyang District Online practice of human resource management. SHRM Beijing, 100027 SHRM Online: shrm.org has more than 575 affiliated chapters within the Phone +86.10.59231033 United States and subsidiary offices in China, India SHRM Research & Surveys: shrm.org/research E-mail [email protected] and United Arab Emirates. Visit us at shrm.org. SHRM Research on Twitter: @SHRM_Research India SHRM Research on LinkedIn: LinkedIn.com Gurgaon, Sector 26 SHRM Research on SHRM Connect: Haryana 122002 community.shrm.org Phone +91.12.44200243 E-mail [email protected] United Arab Emirates Dubai Knowledge Village Dubai, UAE Phone +971.050.104.6330 E-mail [email protected] 16-0156 To order printed copies of this report, visit shrmstore.shrm.org or call 1-800-444-5006. 2 About This Research Report 5 Executive Summary -
Life History Strategies, Population Regulation, and Implications for Fisheries Management1
872 PERSPECTIVE / PERSPECTIVE Life history strategies, population regulation, and implications for fisheries management1 Kirk O. Winemiller Abstract: Life history theories attempt to explain the evolution of organism traits as adaptations to environmental vari- ation. A model involving three primary life history strategies (endpoints on a triangular surface) describes general pat- terns of variation more comprehensively than schemes that examine single traits or merely contrast fast versus slow life histories. It provides a general means to predict a priori the types of populations with high or low demographic resil- ience, production potential, and conformity to density-dependent regulation. Periodic (long-lived, high fecundity, high recruitment variation) and opportunistic (small, short-lived, high reproductive effort, high demographic resilience) strat- egies should conform poorly to models that assume density-dependent recruitment. Periodic-type species reveal greatest recruitment variation and compensatory reserve, but with poor conformity to stock–recruitment models. Equilibrium- type populations (low fecundity, large egg size, parental care) should conform better to assumptions of density- dependent recruitment, but have lower demographic resilience. The model’s predictions are explored relative to sustain- able harvest, endangered species conservation, supplemental stocking, and transferability of ecological indices. When detailed information is lacking, species ordination according to the triangular model provides qualitative -
Spatial Ecology and Modeling of Fish Populations - FAS 6416
Spatial Ecology and Modeling of Fish Populations - FAS 6416 1 Overview Spatial approaches to fisheries management are increasingly of interest the conservation and management of fish populations; and spatial research on fish populations and fish ecology is becoming a cornerstone of fisheries research. This course explores spatial ecology concepts, population dynamics models, examples of field data, and computer simulation tools to understand the ecological origin of spatial fish populations and their importance for fisheries management and conservation. The course is suitable for students who are interested in an interdisciplinary research field that draws as much from ecology as from fisheries management. 2 credits Spring Semester Face-to-face Location TBD with course website: http://elearning.ufl.edu/ The course is intended to provide students with different types of models and concepts that explain the spatial distribution of fish at different spatial and temporal scales. The course is new and evolving, and contains structured elements as well as exploratory exercises and discussions that are aimed at providing students with new perspectives on the dynamics of fish populations. Course Prerequisites: Graduate student status in Fisheries and Aquatic Sciences (or related, as determined by instructor). Interested graduate students in Wildlife Ecology are welcome. Instructor: Dr. Juliane Struve, [email protected]. SFRC Fisheries and Aquatic Sciences Rm. 34, Building 544. Telephone: 352-273-3632. Cell Phone: 352-213-5108 Please use the Canvas message/Inbox feature for fastest response. Office hours: Tuesdays, 3-5pm or online by appointment Textbook(s) and/or readings: The course does not have a designated text book. Reading materials will be assigned for every session and typical consist of 1-2 classic and recent papers. -
Recruitment, Dispersal, and Demographic Rescue in Spatially-Structured White-Tailed Ptarmigan Populations ’
The Condor 102:503-516 D The Cooper Ornithological Society Zoo0 RECRUITMENT, DISPERSAL, AND DEMOGRAPHIC RESCUE IN SPATIALLY-STRUCTURED WHITE-TAILED PTARMIGAN POPULATIONS ’ KATHY MARTIN Canadian Wildlife Service, 5421 Robertson Rd, RR I Delta, British Columbia, Canada, V4K 3N2, and Department of Forest Sciences, 2424 Main Mall, University of British Columbia, Vancouver, British Columbia, Canada, V6T 124, e-mail: [email protected] PETER B. STACEY Department of Biology, University of New Mexico, Albuquerque, NM 87131, e-mail: [email protected] CLAIT E. BRAUN* Colorado Division of Wildlife, 317 West Prospect Rd., Fort Collins, CO 80526, e-mail: [email protected] Abstract. We studiedrecruitment and dispersalof White-tailed Ptarmigan (Lagopus leu- curus) breeding in naturally fragmented alpine habitatsat four study sites in Colorado from 1987-1998. Almost all recruitment for both sexes, particularly females, was of birds pro- duced outside local populationsand also external to nearby studiedpopulations. Populations were more dependent on female recruitment than on male recruitment to sustainthem, and patterns of recruitment were not correlated with local survival of adults or production of young the previous year, except at one site for females. Over 95% of recruitswere yearlings. Breeding dispersal of adults, an infrequent but regular event, was also important to inter- population connectivity. Our data for multiple populationsallowed us to describemovement patterns among populations to assess consistency with conditions required for a -
Talent Acquisition Technology Ecosystem
Global Staffing and Recruitment Technology Talent Acquisition Technology Ecosystem February 15, 2018 | David Francis, Research Manager | [email protected] Talent Acquisition Technology Ecosystem | February 15, 2017 Contents Talent Acquisition Technology Ecosystem ........................................................................................................................................................... 2 Overview of an Ecosystem .................................................................................................................................................................................... 3 Systems of Record ................................................................................................................................................................................................ 8 Candidate Discovery ........................................................................................................................................................................................... 10 Candidate Assessment ........................................................................................................................................................................................ 14 Candidate Engagement ....................................................................................................................................................................................... 15 Candidate Verification ....................................................................................................................................................................................... -
Recruitment of Marine Invertebrates: the Role of Active Larval Choices and Early Mortality
Oecologia (Berl) (1982) 54:348-352 Oer Springer-Verlag 1982 Recruitment of Marine Invertebrates: the Role of Active Larval Choices and Early Mortality Michael J. Keough and Barbara J. Downes Department of Biological Sciences, University of California, Santa Barbara, Ca 93106, USA Summary. Spatial variation in the recruitment of sessile a reflection of the limitations of the observer. The number marine invertebrates with planktonic larvae may be derived of organisms passing through the fourth phase is termed from a number of sources: events within the plankton, recruitment, while the number passing to the third phase choices made by larvae at the time of settlement, and mor- is termed settlement. Recruitment is a composite of larval tality of juvenile organisms after settlement, but before a and juvenile stages, while settlement involves only larval census by an observer. These sources usually are not distin- stages. guished. It is important to distinguish between settlement and A study of the recruitment of four species of sessile recruitment. Non-random patterns of recruitment, such as invertebrates living on rock walls beneath a kelp canopy differences in the density of recruitment with height on the showed that both selection of microhabitats by settling shore (Underwood 1979) or differences in the density of larvae and predation by fish may be important. Two micro- recruitment with patch size (Jackson 1977; Keough 1982a), habitats were of interest; open, flat rock surfaces, and small or with microhabitat, may have two causes: (1) differential pits and crevices that act as refuges from fish predators. settlement, and (2), different probabilities of early mortality The polychaete Spirorbis eximus and the cyclostome in different parts of an organism's habitat. -
A Global Method for Calculating Plant CSR Ecological Strategies Applied Across Biomes World-Wide
Functional Ecology 2016 doi: 10.1111/1365-2435.12722 A global method for calculating plant CSR ecological strategies applied across biomes world-wide Simon Pierce*,1, Daniel Negreiros2, Bruno E. L. Cerabolini3, Jens Kattge4, Sandra Dıaz5, Michael Kleyer6, Bill Shipley7, Stuart Joseph Wright8, Nadejda A. Soudzilovskaia9, Vladimir G. Onipchenko10, Peter M. van Bodegom9, Cedric Frenette-Dussault7, Evan Weiher11, Bruno X. Pinho12, Johannes H. C. Cornelissen13, John Philip Grime14, Ken Thompson14, Roderick Hunt15, Peter J. Wilson14, Gabriella Buffa16, Oliver C. Nyakunga16,17, Peter B. Reich18,19, Marco Caccianiga20, Federico Mangili20, Roberta M. Ceriani21, Alessandra Luzzaro1, Guido Brusa3, Andrew Siefert22, Newton P. U. Barbosa2, Francis Stuart Chapin III23, William K. Cornwell24, Jingyun Fang25, Geraldo Wilson Fernandez2,26, Eric Garnier27, Soizig Le Stradic28, Josep Penuelas~ 29,30, Felipe P. L. Melo12, Antonio Slaviero16, Marcelo Tabarelli12 and Duccio Tampucci20 1Department of Agricultural and Environmental Sciences (DiSAA), University of Milan, Via G. Celoria 2, I-20133 Milan, Italy; 2Ecologia Evolutiva e Biodiversidade/DBG, ICB/Universidade Federal de Minas Gerais, CP 486, 30161-970 Belo Horizonte, MG, Brazil; 3Department of Theoretical and Applied Sciences, University of Insubria, Via J.H. Dunant 3, I-21100 Varese, Italy; 4Max Planck Institute for Biogeochemistry, P.O. Box 100164, 07701 Jena, Germany; 5Instituto Multidisciplinario de Biologıa Vegetal (CONICET-UNC) and FCEFyN, Universidad Nacional de Cordoba, Av. Velez Sarsfield 299, -
Conservation Ecology: Using Ants As Bioindicators
Table of Contents Using Ants as bioindicators: Multiscale Issues in Ant Community Ecology......................................................0 ABSTRACT...................................................................................................................................................0 INTRODUCTION.........................................................................................................................................0 SCALE DEPENDENCY IN ANT COMMUNITIES....................................................................................1 Functional groups .............................................................................................................................2 Regulation of diversity......................................................................................................................3 Measuring species richness and composition...................................................................................4 Estimating species richness...............................................................................................................6 IMPLICATIONS FOR THE USE OF ANTS AS BIOINDICATORS..........................................................8 Using functional groups to assess ecological change.......................................................................8 Assessing species diversity...............................................................................................................9 RESPONSES TO THIS ARTICLE.............................................................................................................10