Meta-Ecosystems: a Theoretical Framework for a Spatial Ecosystem Ecology
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An Assessment of Marine Ecosystem Damage from the Penglai 19-3 Oil Spill Accident
Journal of Marine Science and Engineering Article An Assessment of Marine Ecosystem Damage from the Penglai 19-3 Oil Spill Accident Haiwen Han 1, Shengmao Huang 1, Shuang Liu 2,3,*, Jingjing Sha 2,3 and Xianqing Lv 1,* 1 Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao 266100, China; [email protected] (H.H.); [email protected] (S.H.) 2 North China Sea Environment Monitoring Center, State Oceanic Administration (SOA), Qingdao 266033, China; [email protected] 3 Department of Environment and Ecology, Shandong Province Key Laboratory of Marine Ecology and Environment & Disaster Prevention and Mitigation, Qingdao 266100, China * Correspondence: [email protected] (S.L.); [email protected] (X.L.) Abstract: Oil spills have immediate adverse effects on marine ecological functions. Accurate as- sessment of the damage caused by the oil spill is of great significance for the protection of marine ecosystems. In this study the observation data of Chaetoceros and shellfish before and after the Penglai 19-3 oil spill in the Bohai Sea were analyzed by the least-squares fitting method and radial basis function (RBF) interpolation. Besides, an oil transport model is provided which considers both the hydrodynamic mechanism and monitoring data to accurately simulate the spatial and temporal distribution of total petroleum hydrocarbons (TPH) in the Bohai Sea. It was found that the abundance of Chaetoceros and shellfish exposed to the oil spill decreased rapidly. The biomass loss of Chaetoceros and shellfish are 7.25 × 1014 ∼ 7.28 × 1014 ind and 2.30 × 1012 ∼ 2.51 × 1012 ind in the area with TPH over 50 mg/m3 during the observation period, respectively. -
Energy Economics in Ecosystems By: J
Energy Economics in Ecosystems By: J. Michael Beman (School of Natural Sciences, University of California at Merced) © 2010 Nature Education Citation: Beman, J. (2010) Energy Economics in Ecosystems. Nature Education Knowledge 3(10):13 What powers life? In most ecosystems, sunlight is absorbed and converted into usable forms of energy via photosynthesis. These usable forms of energy are carbonbased. The laws of physics describe the interactions between energy and mass: the energy in a closed system is conserved, and matter can neither be created nor destroyed. Modern physics has shown that reality is more 2 complex at very large and very small scales (e.g., Einstein’s famous equation, E = mc demonstrated that mass can be converted to energy in the sun or nuclear reactors), but in the context of Earth’s ecosystems, energy is conserved and matter can neither be created nor destroyed. This seemingly simplistic statement has profound consequences when we study how ecosystems function. In particular, the energy present within an ecosystem is collected and shared by organisms in many different ways; this "sharing" takes place through ecological interactions, such as predatorprey dynamics and symbioses. When we move to the ecosystem level, however, we consider interactions among organisms, populations, communities, and their physical and chemical environment. These interactions have an important bearing on the structure of organisms, ecosystems, and, over geologic time, the planet itself. A primary example of this involves the energy currency in ecosystems, which is carbon. The amount and form of carbon present in different ecosystem pools — such as plants, animals, air, soil, and water — is controlled by organisms, and ultimately affects their ecological success. -
Maximum Sustainable Yield from Interacting Fish Stocks in an Uncertain World: Two Policy Choices and Underlying Trade-Offs Arxiv
Maximum sustainable yield from interacting fish stocks in an uncertain world: two policy choices and underlying trade-offs Adrian Farcas Centre for Environment, Fisheries & Aquaculture Science Pakefield Road, Lowestoft NR33 0HT, United Kingdom [email protected] Axel G. Rossberg∗ Queen Mary University of London, School of Biological and Chemical Sciences, 327 Mile End Rd, London E1, United Kingdom and Centre for Environment, Fisheries & Aquaculture Science Pakefield Road, Lowestoft NR33 0HT, United Kingdom [email protected] 26 May 2016 c Crown copyright Abstract The case of fisheries management illustrates how the inherent structural instability of ecosystems can have deep-running policy implications. We contrast ten types of management plans to achieve maximum sustainable yields (MSY) from multiple stocks and compare their effectiveness based on a management strategy evalua- tion (MSE) that uses complex food webs in its operating model. Plans that target specific stock sizes (BMSY) consistently led to higher yields than plans targeting spe- cific fishing pressures (FMSY). A new self-optimising control rule, introduced here arXiv:1412.0199v6 [q-bio.PE] 31 May 2016 for its robustness to structural instability, led to intermediate yields. Most plans outperformed single-species management plans with pressure targets set without considering multispecies interactions. However, more refined plans to \maximise the yield from each stock separately", in the sense of a Nash equilibrium, produced total yields comparable to plans aiming to maximise total harvested biomass, and were more robust to structural instability. Our analyses highlight trade-offs between yields, amenability to negotiations, pressures on biodiversity, and continuity with current approaches in the European context. -
Vegetation Demographics in Earth System Models: a Review of Progress and Priorities
Lawrence Berkeley National Laboratory Recent Work Title Vegetation demographics in Earth System Models: A review of progress and priorities. Permalink https://escholarship.org/uc/item/3912p4m3 Journal Global change biology, 24(1) ISSN 1354-1013 Authors Fisher, Rosie A Koven, Charles D Anderegg, William RL et al. Publication Date 2018 DOI 10.1111/gcb.13910 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Received: 11 April 2017 | Revised: 12 August 2017 | Accepted: 17 August 2017 DOI: 10.1111/gcb.13910 RESEARCH REVIEW Vegetation demographics in Earth System Models: A review of progress and priorities Rosie A. Fisher1 | Charles D. Koven2 | William R. L. Anderegg3 | Bradley O. Christoffersen4 | Michael C. Dietze5 | Caroline E. Farrior6 | Jennifer A. Holm2 | George C. Hurtt7 | Ryan G. Knox2 | Peter J. Lawrence1 | Jeremy W. Lichstein8 | Marcos Longo9 | Ashley M. Matheny10 | David Medvigy11 | Helene C. Muller-Landau12 | Thomas L. Powell2 | Shawn P. Serbin13 | Hisashi Sato14 | Jacquelyn K. Shuman1 | Benjamin Smith15 | Anna T. Trugman16 | Toni Viskari12 | Hans Verbeeck17 | Ensheng Weng18 | Chonggang Xu4 | Xiangtao Xu19 | Tao Zhang8 | Paul R. Moorcroft20 1National Center for Atmospheric Research, Boulder, CO, USA 2Lawrence Berkeley National Laboratory, Berkeley, CA, USA 3Department of Biology, University of Utah, Salt Lake City, UT, USA 4Los Alamos National Laboratory, Los Alamos, NM, USA 5Department of Earth and Environment, Boston University, Boston, MA, USA 6Department of Integrative Biology, -
Synthetic Mutualism and the Intervention Dilemma
life Review Synthetic Mutualism and the Intervention Dilemma Jai A. Denton 1,† ID and Chaitanya S. Gokhale 2,*,† ID 1 Genomics and Regulatory Systems Unit, Okinawa Institute of Science and Technology, Onna-son 904-0412, Japan; [email protected] 2 Research Group for Theoretical models of Eco-Evolutionary Dynamics, Max Planck Institute for Evolutionary Biology, 24304 Plön, Germany * Correspondence: [email protected]; Tel.: +49-45-2276-3574 † These authors contributed equally to this work. Received: 30 October 2018; Accepted: 23 January 2019; Published: 28 January 2019 Abstract: Ecosystems are complex networks of interacting individuals co-evolving with their environment. As such, changes to an interaction can influence the whole ecosystem. However, to predict the outcome of these changes, considerable understanding of processes driving the system is required. Synthetic biology provides powerful tools to aid this understanding, but these developments also allow us to change specific interactions. Of particular interest is the ecological importance of mutualism, a subset of cooperative interactions. Mutualism occurs when individuals of different species provide a reciprocal fitness benefit. We review available experimental techniques of synthetic biology focused on engineered synthetic mutualistic systems. Components of these systems have defined interactions that can be altered to model naturally occurring relationships. Integrations between experimental systems and theoretical models, each informing the use or development of the other, allow predictions to be made about the nature of complex relationships. The predictions range from stability of microbial communities in extreme environments to the collapse of ecosystems due to dangerous levels of human intervention. With such caveats, we evaluate the promise of synthetic biology from the perspective of ethics and laws regarding biological alterations, whether on Earth or beyond. -
Introduction to Theoretical Ecology
Introduction to Theoretical Ecology Natal, 2011 Objectives After this week: The student understands the concept of a biological system in equilibrium and knows that equilibria can be stable or unstable. The student understands the basics of how coupled differential equations can be analyzed graphically, including phase plane analysis and nullclines. The student can analyze the stability of the equilibria of a one-dimensional differential equation model graphically. The student has a basic understanding of what a bifurcation point is. The student can relate alternative stable states to a 1D bifurcation plot (e.g. catastrophe fold). Study material / for further study: This text Scheffer, M. 2009. Critical Transitions in Nature and Society, Princeton University Press, Princeton and Oxford. Scheffer, M. 1998. Ecology of Shallow Lakes. 1 edition. Chapman and Hall, London. Edelstein-Keshet, L. 1988. Mathematical models in biology. 1 edition. McGraw-Hill, Inc., New York. Tentative programme (maybe too tight for the exercises) Monday 9:00-10:30 Introduction Modelling + introduction Forrester diagram + 1D models (stability graphs) 10:30-13:00 GRIND Practical CO2 chamber - Ethiopian Wolf Tuesday 9:00-10:00 Introduction bifurcation (Allee effect) and Phase plane analysis (Lotka-Volterra competition) 10:00-13:00 GRIND Practical Lotka-Volterra competition + Sahara Wednesday 9:00-13:00 GRIND Practical – Sahara (continued) and Algae-zooplankton Thursday 9:00-13:00 GRIND practical – Algae zooplankton spatial heterogeneity Friday 9:00-12:00 GRIND practical- Algae zooplankton fish 12:00-13:00 Practical summary/explanation of results - Wrap up 1 An introduction to models What is a model? The word 'model' is used widely in every-day language. -
Emergent Biogeography of Microbial Communities in a Model Ocean
REPORTS germ insects not only uncovers those features es- mRNA localization indeed appears to be an sential to this developmental mode but also sheds important component of long-germ embryogene- light on how the bcd-dependent anterior patterning sis, perhaps even playing a role in the transition program might have evolved. Through analysis of from the ancestral short-germ to the derived long- the regulation of the trunk gap gene Kr in Dro- germ fate. sophila and Nasonia,wehavebeenabletodem- onstrate that anterior repression of Kr is essential References and Notes for head and thorax formation and is a common 1. G. K. Davis, N. H. Patel, Annu. Rev. Entomol. 47, 669 (2002). feature of long-germ patterning. Both insects 2. T. Berleth et al., EMBO J. 7, 1749 (1988). accomplish this task through maternal, anteriorly 3. W. Driever, C. Nusslein-Volhard, Cell 54, 83 (1988). localized factors that either indirectly (Drosophila) 4. J. Lynch, C. Desplan, Curr. Biol. 13, R557 (2003). or directly (Nasonia) repress Kr and, hence, trunk 5. J. A. Lynch, A. E. Brent, D. S. Leaf, M. A. Pultz, C. Desplan, Nature 439, 728 (2006). fates. In Drosophila, the terminal system and bcd 6. J. Savard et al., Genome Res. 16, 1334 (2006). regulate expression of gap genes, including Dm-gt, 7. G. Struhl, P. Johnston, P. A. Lawrence, Cell 69, 237 (1992). that repress Dm-Kr. Nasonia’s bcd-independent 8. A. Preiss, U. B. Rosenberg, A. Kienlin, E. Seifert, long-germ embryos must solve the same problem, H. Jackle, Nature 313, 27 (1985). Fig. 4. -
Chapter 4 – Steady State Models
CHAPTER 4 Steady State Models X.-Z. Kong, F.-L. Xu1, W. He, W.-X. Liu, B. Yang Peking University, Beijing, China 1Corresponding author: E-mail: xufl@urban.pku.edu.cn OUTLINE 4.1 Steady State Model: Ecopath as an 4.2.4.2 Changes in Ecosystem Example 66 Functioning 79 4.1.1 Steady State Model 66 4.2.4.3 Collapse in the Food 4.1.2 Ecopath Model 66 Web: Differences in 4.1.3 Future Perspectives 68 Structure 81 4.2.4.4 Toward an Immature 4.2 Ecopath Model for a Large Chinese but Stable Ecosystem 83 Lake: A Case Study 68 4.2.4.5 Potential Driving 4.2.1 Introduction 68 Factors and 4.2.2 Study Site 70 Underlying 4.2.3 Model Development 73 Mechanisms 84 4.2.3.1 Model Construction 4.2.4.6 Hints for Future Lake and Parameterization 73 Fishery and 4.2.3.2 Evaluation of Ecosystem Restoration 85 Functioning 74 4.2.3.3 Determination of 4.3 Conclusions 86 Trophic Level 76 References 86 4.2.4 Results and Discussion 76 4.2.4.1 Basic Model Performance 76 Ecological Model Types, Volume 28 Ó 2016 Elsevier B.V. http://dx.doi.org/10.1016/B978-0-444-63623-2.00004-9 65 All rights reserved. 66 4. STEADY STATE MODELS 4.1 STEADY STATE MODEL: ECOPATH AS AN EXAMPLE 4.1.1 Steady State Model Steady state ecological models are established to describe conditions in which the modeled components (mass or energy) are stable, i.e., do not change over time (Jørgensen and Fath, 2011). -
Biogeography: an Ecosystems Approach (Geography 338)
WELCOME TO GEOGRAPHY/BOTANY 338: ENVIRONMENTAL BIOGEOGRAPHY Fall 2018 Schedule: Monday & Wednesday 2:30-3:45 pm, Humanities 1641 Credits: 3 Instructor: Professor Ken Keefover-Ring Email: [email protected] Office: Science Hall 115C Office Hours: Tuesday 3:00-4:00 pm & Wednesday 12:00-1:00 pm or by appointment Note: This course fulfills the Biological Science breadth requirement. COURSE DESCRIPTION: This course takes an ecosystems approach to understand how physical -- climate, geologic history, soils -- and biological -- physiology, evolution, extinction, dispersal, competition, predation -- factors interact to affect the past, present and future distribution of terrestrial biomes and all levels of biodiversity: ecosystems, species and genes. A particular focus will be placed on the role of disturbance and to recent human-driven climatic and land-cover changes and biological invasions on differences in historical and current distributions of global biodiversity. COURSE GOALS: • To learn patterns and mechanisms of local to global gene, species, ecosystem and biome distributions • To learn how past, current and future environmental change affect biogeography • To learn how humans affect geographic patterns of biodiversity • To learn how to apply concepts from biogeography to current environmental problems • To learn how to read and interpret the primary literature, that is, scientific articles in peer- reviewed journals. COURSE POLICY: I expect you to attend all lectures and come prepared to participate in discussion. I will take attendance. Please let me know if you need to miss three or more lectures. Please respect your fellow students, professor, and guest speakers and turn off the ringers on your cell phones and refrain from texting during class time. -
Models for an Ecosystem Approach to Fisheries
ISSN 0429-9345 FAO FISHERIES 477 TECHNICAL PAPER 477 Models for an ecosystem approach to fisheries Models for an ecosystem approach to fisheries This report reviews the methods available for assessing the impacts of interactions between species and fisheries and their implications for marine fisheries management. A brief description of the various modelling approaches currently in existence is provided, highlighting in particular features of these models that have general relevance to the field of ecosystem approach to fisheries (EAF). The report concentrates on the currently available models representative of general types such as bionergetic models, predator-prey models and minimally realistic models. Short descriptions are given of model parameters, assumptions and data requirements. Some of the advantages, disadvantages and limitations of each of the approaches in addressing questions pertaining to EAF are discussed. The report concludes with some recommendations for moving forward in the development of multispecies and ecosystem models and for the prudent use of the currently available models as tools for provision of scientific information on fisheries in an ecosystem context. FAO Cover: Illustration by Elda Longo FAO FISHERIES Models for an ecosystem TECHNICAL PAPER approach to fisheries 477 by Éva E. Plagányi University of Cape Town South Africa FOOD AND AGRICULTURE AND ORGANIZATION OF THE UNITED NATIONS Rome, 2007 The designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. -
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. -
Plant Species Traits Are the Predominant Control on Litter Decomposition Rates Within Biomes Worldwide
Ecology Letters, (2008) 11: 1065–1071 doi: 10.1111/j.1461-0248.2008.01219.x LETTER Plant species traits are the predominant control on litter decomposition rates within biomes worldwide Abstract William K. Cornwell,1* Johannes Worldwide decomposition rates depend both on climate and the legacy of plant functional H. C. Cornelissen,1 Kathryn traits as litter quality. To quantify the degree to which functional differentiation among Amatangelo,2 Ellen Dorrepaal,1 species affects their litter decomposition rates, we brought together leaf trait and litter mass 3 4 Valerie T. Eviner, Oscar Godoy, loss data for 818 species from 66 decomposition experiments on six continents. We show Sarah E. Hobbie,5 Bart Hoorens,1 6,7 that: (i) the magnitude of species-driven differences is much larger than previously thought Hiroko Kurokawa, Natalia and greater than climate-driven variation; (ii) the decomposability of a speciesÕ litter is Pe´ rez-Harguindeguy,8 Helen M. consistently correlated with that speciesÕ ecological strategy within different ecosystems Quested,9 Louis S. Santiago,10 David A. Wardle,11,12 Ian J. globally, representing a new connection between whole plant carbon strategy and Wright,13 Rien Aerts,1 Steven D. biogeochemical cycling. This connection between plant strategies and decomposability is Allison,14 Peter van Bodegom,1 crucial for both understanding vegetation–soil feedbacks, and for improving forecasts of Victor Brovkin,15 Alex Chatain,16 the global carbon cycle. Terry V. Callaghan,17,18 Sandra Dı´az,7 Eric Garnier,19 Diego E. Keywords Gurvich,8 Elena Kazakou,19 Julia Carbon cycling, decomposition, leaf economic spectrum, leaf traits, meta-analysis.