Theoretical Ecology a Unified Approach
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
Population Ecology: Theory, Methods, Lenses Dr. Bill Fagan
Population ecology: Theory, methods, lenses Dr. Bill Fagan Population Ecology & Spatial Ecology A) Core principles of population growth B) Spatial problems and methods for modeling them C) Integrodifference equations as a robust platform Population Ecology & Spatial Ecology A) Core principles of population growth B) Spatial problems and methods for modeling them C) Integrodifference equations as a robust platform Socio – Environmental Issues: 1. Fisheries 2. Invasive Species 3. Biological Control 4. Ecological Footprints 5. Critical Patch Size / Reserve Design A) Core principles of population growth Berryman: On principles, laws, and theory in population ecology. Oikos. 2003 1) Exponential population growth as a null baseline. What causes deviations from that ? The Basics of Discrete Time Models Have Form Nt1 f Nt , Nt1, Nt2 ,... where N is the thing you are measuring and t is an index representing blocks of time. Constant time step = 1 unit (year, month, day, second) Time is discrete, #’s need not be In many cases Nt1 f Nt Reduced Form Status next time step depends only on where system is now. history is unimportant Alternatively: N t 1 f N t , N t 1 , N t 2 ... history is important wide applicability 1) Many ecological phenomenon change discretely - insects don’t hatch out all day long, only in morning - rodents are less mobile near full moon - seeds germinate in spring daily censuses 2) Data were collected at discrete times yearly censuses The Simplest Discrete Time Model N t1 N t “Geometric” Growth Equation N Thing we -
Concept of Population Ecology
Mini Review Int J Environ Sci Nat Res Volume 12 Issue 1 - June 2018 Copyright © All rights are reserved by Nida Tabassum Khan DOI: 10.19080/IJESNR.2018.12.555828 Concept of Population Ecology Nida Tabassum Khan* Department of Biotechnology, Faculty of Life Sciences and Informatics, Balochistan University of Information Technology Engineering and Management Sciences, Pakistan Submission: May 29, 2018; Published: June 06, 2018 *Corresponding author: Nida Tabassum Khan, Department of Biotechnology, Balochistan University, Quetta, Pakistan, Tel: ; Email: Abstract Population ecology deals with the study of the structure and subtleties of a population which comprises of a group of interacting organisms of the same specie that occupies a given area. The demographic structure of a population is a key factor which is characterized by the number of is growing, shrinking or remain constant in terms of its size. individual members (population size) present at each developmental stage of their cycle to identify whether the population of a specific specie Keywords: Population size; Population density; Gene pool; Life tables Introduction of random genetic drift therefore variation is easily sustained Population ecology deals with the study of the structure in large populations than in smaller ones [10]. Natural selection and subtleties of a population which comprises of a group of selects the most favorable phenotypes suited for an organism interacting organisms of the same specie that occupies a given survival thereby reducing variation within populations [11]. area [1]. Populations can be characterized as local which is a Population structure determines the arrays of demographic group of less number of individuals occupying a small area or variation such as mode of reproduction, age, reproduction met which is a group of local populations linked by disbanding frequency, offspring counts, gender ratio of newborns etc within/ members [2]. -
Lecture 33 May 9 Species Interactions – Competition 2007
Figure 49.14 upper left 7.014 Lecture 33 May 9 Species Interactions – Competition 2007 Consumptive competition occurs when organisms compete for the same resources. These trees are competing for nitrogen and other nutrients. Figure 49.14 upper right Figure 49.14 middle left Preemptive competition occurs when individuals occupy space and prevent access Overgrowth competition occurs when an organism grows over another, blocking to resources by other individuals. The space preempted by these barnacles is access to resources. This large fern has overgrown other individuals and is unavailable to competitors. shading them. 1 Figure 49.14 middle right Figure 49.14 lower left Chemical competition occurs when one species produces toxins that negatively Territorial competition occurs when mobile organisms protect a feeding or affect another. Note how few plants are growing under these Salvia shrubs. breeding territory. These red-winged blackbirds are displaying to each other at a territorial boundary. Figure 49.14 lower left The Fundamental Ecological Niche: “An n-dimensional hyper-volume every point on which a species can survive and reproduce indefinitely in the absence of other species” (Hutchinson) y t i d i m u h e iz tem s pe d Encounter competition occurs when organisms interfere directly with each other’s ra oo tur F access to specific resources. Here, spotted hyenas and vultures fight over a kill. e 2 The Realized Ecological Niche: the niche actually occupied in the presence of other species niche overlap leads to competition y t i d i -
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. -
Provided for Non-Commercial Research and Educational Use
Provided for non-commercial research and educational use. Not for reproduction, distribution or commercial use. This article was originally published in the Encyclopedia of Ecology, Volumes 1-5 published by Elsevier, and the attached copy is provided by Elsevier for the author’s benefit and for the benefit of the author’s institution, for non-commercial research and educational use including without limitation use in instruction at your institution, sending it to specific colleagues who you know, and providing a copy to your institution’s administrator. All other uses, reproduction and distribution, including without limitation commercial reprints, selling or licensing copies or access, or posting on open internet sites, your personal or institution’s website or repository, are prohibited. For exceptions, permission may be sought for such use through Elsevier’s permissions site at: http://www.elsevier.com/locate/permissionusematerial M Scotti. Development Capacity. In Sven Erik Jørgensen and Brian D. Fath (Editor-in-Chief), Ecological Indicators. Vol. [2] of Encyclopedia of Ecology, 5 vols. pp. [911-920] Oxford: Elsevier. Author's personal copy Ecological Indicators | Development Capacity 911 animals that appear on and in dung and the processes they Cross WF, Benstead JP, Frost PC, and Thomas SA (2005) Ecological stoichiometry in freshwater benthic systems: Recent progress and initiate are highly predictable, but in detail depend on the perspectives. Freshwater Biology 50: 1895–1912. habitat and climate under investigation. Specialized copro- Findlay S and Sinsabaugh R (eds.) (2000) Dissolved Organic Matter in philous fungi, similarly, exhibit a clear sequence of Aquatic Ecosystems. San Diego: Academic Press. Flindt MR, Pardal MA, Lillebø AI, Martins I, and Marques JC (1999) utilization of their habitat, spores of early stages already Nutrient cycling and plant dynamics in estuaries: A brief review. -
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. -
Ascendency As Ecological Indicator for Environmental Quality Assessment at the Ecosystem Level: a Case Study
Hydrobiologia (2006) 555:19–30 Ó Springer 2006 H. Queiroga, M.R. Cunha, A. Cunha, M.H. Moreira, V. Quintino, A.M. Rodrigues, J. Seroˆ dio & R.M. Warwick (eds), Marine Biodiversity: Patterns and Processes, Assessment, Threats, Management and Conservation DOI 10.1007/s10750-005-1102-8 Ascendency as ecological indicator for environmental quality assessment at the ecosystem level: a case study J. Patrı´ cio1,*, R. Ulanowicz2, M. A. Pardal1 & J. C. Marques1 1IMAR- Institute of Marine Research, Department of Zoology, Faculty of Sciences and Technology, University of Coimbra, 3004-517, Coimbra, Portugal 2Chesapeake Biological Laboratory, Center for Environmental and Estuarine Studies, University of Maryland, Solomons, Maryland, 20688-0038, USA (*Author for correspondence: E-mail: [email protected]) Key words: network analysis, ascendency, eutrophication, estuary Abstract Previous studies have shown that when an ecosystem consists of many interacting components it becomes impossible to understand how it functions by focussing only on individual relationships. Alternatively, one can attempt to quantify system behaviour as a whole by developing ecological indicators that combine numerous environmental factors into a single value. One such holistic measure, called the system ‘ascen- dency’, arises from the analysis of networks of trophic exchanges. It deals with the joint quantification of overall system activity with the organisation of the component processes and can be used specifically to identify the occurrence of eutrophication. System ascendency analyses were applied to data over a gradient of eutrophication in a well documented small temperate intertidal estuary. Three areas were compared along the gradient, respectively, non eutrophic, intermediate eutrophic, and strongly eutrophic. Values of other measures related to the ascendency, such as the total system throughput, development capacity, and average mutual information, as well as the ascendency itself, were clearly higher in the non-eutrophic area. -
How to Quantify Competitive Ability
Received: 7 December 2017 | Accepted: 8 February 2018 DOI: 10.1111/1365-2745.12954 ESSAY REVIEW How to quantify competitive ability Simon P. Hart1 | Robert P. Freckleton2 | Jonathan M. Levine1 1Institute of Integrative Biology, ETH Zürich (Swiss Federal Institute of Technology), Abstract Zürich, Switzerland 1. Understanding the role of competition in structuring communities requires that we 2 Department of Animal and Plant quantify competitive ability in a way that permits us to predict the outcome of com- Sciences, University of Sheffield, Sheffield, UK petition over the long term. Given such a clear goal for a process that has been the focus of ecological research for decades, there is surprisingly little consensus on how Correspondence Simon P. Hart to measure competitive ability, with up to 50 different metrics currently proposed. Email: [email protected] 2. Using competitive population dynamics as a foundation, we define competitive Handling Editor: Hans de Kroon ability—the ability of one species to exclude another—using quantitative theoreti- cal models of population dynamics to isolate the key parameters that are known to predict competitive outcomes. 3. Based on the definition of competitive ability we identify the empirical require- ments and describe straightforward methods for quantifying competitive ability in future empirical studies. In doing so, our analysis also allows us to identify why many existing approaches to studying competition are unsuitable for quantifying competitive ability. 4. Synthesis. Competitive ability is precisely defined starting from models of com- petitive population dynamics. Quantifying competitive ability in a theoretically justified manner is straightforward using experimental designs readily applied to studies of competition in the laboratory and field. -
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, -
Can More K-Selected Species Be Better Invaders?
Diversity and Distributions, (Diversity Distrib.) (2007) 13, 535–543 Blackwell Publishing Ltd BIODIVERSITY Can more K-selected species be better RESEARCH invaders? A case study of fruit flies in La Réunion Pierre-François Duyck1*, Patrice David2 and Serge Quilici1 1UMR 53 Ӷ Peuplements Végétaux et ABSTRACT Bio-agresseurs en Milieu Tropical ӷ CIRAD Invasive species are often said to be r-selected. However, invaders must sometimes Pôle de Protection des Plantes (3P), 7 chemin de l’IRAT, 97410 St Pierre, La Réunion, France, compete with related resident species. In this case invaders should present combina- 2UMR 5175, CNRS Centre d’Ecologie tions of life-history traits that give them higher competitive ability than residents, Fonctionnelle et Evolutive (CEFE), 1919 route de even at the expense of lower colonization ability. We test this prediction by compar- Mende, 34293 Montpellier Cedex, France ing life-history traits among four fruit fly species, one endemic and three successive invaders, in La Réunion Island. Recent invaders tend to produce fewer, but larger, juveniles, delay the onset but increase the duration of reproduction, survive longer, and senesce more slowly than earlier ones. These traits are associated with higher ranks in a competitive hierarchy established in a previous study. However, the endemic species, now nearly extinct in the island, is inferior to the other three with respect to both competition and colonization traits, violating the trade-off assumption. Our results overall suggest that the key traits for invasion in this system were those that *Correspondence: Pierre-François Duyck, favoured competition rather than colonization. CIRAD 3P, 7, chemin de l’IRAT, 97410, Keywords St Pierre, La Réunion Island, France. -
Community-Based Population Recovery of Overexploited Amazonian Wildlife
G Model PECON-41; No. of Pages 5 ARTICLE IN PRESS Perspectives in Ecology and Conservation xxx (2017) xxx–xxx ´ Supported by Boticario Group Foundation for Nature Protection www.perspectecolconserv.com Policy Forums Community-based population recovery of overexploited Amazonian wildlife a,∗ b c d e João Vitor Campos-Silva , Carlos A. Peres , André P. Antunes , João Valsecchi , Juarez Pezzuti a Institute of Biological and Health Sciences, Federal University of Alagoas, Av. Lourival Melo Mota, s/n, Tabuleiro do Martins, Maceió 57072-900, AL, Brazil b Centre for Ecology, Evolution and Conservation, School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR47TJ, UK c Wildlife Conservation Society Brasil, Federal University of Amazonas, Av. Rodrigo Otavio, 3000, Manaus 69077-000, Amazonas, Brazil d Mamiraua Sustainable Development Institute (IDSM), Estrada da Bexiga 2584, Fonte Boa, Tefé, AM, Brazil e Centre for Advanced Amazon Studies, Federal University of Para, R. Augusto Correa 01, CEP 66075-110, Belém, PA, Brazil a b s t r a c t a r t i c l e i n f o Article history: The Amazon Basin experienced a pervasive process of resource overexploitation during the 20th-century, Received 3 June 2017 which induced severe population declines of many iconic vertebrate species. In addition to biodiversity Accepted 18 August 2017 loss and the ecological consequences of defaunation, food security of local communities was relentlessly threatened because wild meat had a historically pivotal role in protein acquisition by local dwellers. Keywords: Here we discuss the urgent need to regulate subsistence hunting by Amazonian semi-subsistence local Hunting regulation communities, which are far removed from the market and information economy.