Density-Independent Population Growth
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Body Size and Biomass Distributions of Carrion Visiting Beetles: Do Cities Host Smaller Species?
Ecol Res (2008) 23: 241–248 DOI 10.1007/s11284-007-0369-9 ORIGINAL ARTICLE Werner Ulrich Æ Karol Komosin´ski Æ Marcin Zalewski Body size and biomass distributions of carrion visiting beetles: do cities host smaller species? Received: 15 November 2006 / Accepted: 14 February 2007 / Published online: 28 March 2007 Ó The Ecological Society of Japan 2007 Abstract The question how animal body size changes Introduction along urban–rural gradients has received much attention from carabidologists, who noticed that cities harbour Animal and plant body size is correlated with many smaller species than natural sites. For Carabidae this aspects of life history traits and species interactions pattern is frequently connected with increasing distur- (dispersal, reproduction, energy intake, competition; bance regimes towards cities, which favour smaller Brown et al. 2004; Brose et al. 2006). Therefore, species winged species of higher dispersal ability. However, body size distributions (here understood as the fre- whether changes in body size distributions can be gen- quency distribution of log body size classes, SSDs) are eralised and whether common patterns exist are largely often used to infer patterns of species assembly and unknown. Here we report on body size distributions of energy use (Peters 1983; Calder 1984; Holling 1992; carcass-visiting beetles along an urban–rural gradient in Gotelli and Graves 1996; Etienne and Olff 2004; Ulrich northern Poland. Based on samplings of 58 necrophages 2005a, 2006). and 43 predatory beetle species, mainly of the families Many of the studies on local SSDs focused on the Catopidae, Silphidae, and Staphylinidae, we found number of modes and the shape. -
Globalization and Infectious Diseases: a Review of the Linkages
TDR/STR/SEB/ST/04.2 SPECIAL TOPICS NO.3 Globalization and infectious diseases: A review of the linkages Social, Economic and Behavioural (SEB) Research UNICEF/UNDP/World Bank/WHO Special Programme for Research & Training in Tropical Diseases (TDR) The "Special Topics in Social, Economic and Behavioural (SEB) Research" series are peer-reviewed publications commissioned by the TDR Steering Committee for Social, Economic and Behavioural Research. For further information please contact: Dr Johannes Sommerfeld Manager Steering Committee for Social, Economic and Behavioural Research (SEB) UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR) World Health Organization 20, Avenue Appia CH-1211 Geneva 27 Switzerland E-mail: [email protected] TDR/STR/SEB/ST/04.2 Globalization and infectious diseases: A review of the linkages Lance Saker,1 MSc MRCP Kelley Lee,1 MPA, MA, D.Phil. Barbara Cannito,1 MSc Anna Gilmore,2 MBBS, DTM&H, MSc, MFPHM Diarmid Campbell-Lendrum,1 D.Phil. 1 Centre on Global Change and Health London School of Hygiene & Tropical Medicine Keppel Street, London WC1E 7HT, UK 2 European Centre on Health of Societies in Transition (ECOHOST) London School of Hygiene & Tropical Medicine Keppel Street, London WC1E 7HT, UK TDR/STR/SEB/ST/04.2 Copyright © World Health Organization on behalf of the Special Programme for Research and Training in Tropical Diseases 2004 All rights reserved. The use of content from this health information product for all non-commercial education, training and information purposes is encouraged, including translation, quotation and reproduction, in any medium, but the content must not be changed and full acknowledgement of the source must be clearly stated. -
Modeling Techniques
Appendix A Modeling Techniques A.1 Population Growth Models Using Differential Equations Our main goal here is to introduce a few modeling techniques we use throughout this book. We do not intend however to provide here the fundamentals on modeling, a tutorial or a review. For these, we refer to other sources (DeAngelis et al. 1992; Ford 2009; Grimm et al. 2006; Kuang 1993). This Appendix is rather a refresher as well as an example of why using different modeling techniques for one and the same problem can be beneficial to understand biological processes better. We start with the simple exponential population growth to make modeling accessible even to complete beginners. Biologists generally define a population as a collection of individuals that belong to the same species and can potentially breed with each other. One of the best-known early models on population growth was outlined by Malthus (1798). He famously maintained that the human population is predicted to grow in an exponential manner, but the crucial products needed to sustain the population grow in but a linear manner. He argued that these different types of growths will trigger disasters when the population’s needs are not satisfied. The basic exponential growth model consists of a single positive feedback loop that arises from the fact that every individual (N) is predicted to have a fixed number of offspring (r), regardless of the size of the population, and thus also regardless of the remaining resources in the habitat: dN = rN dt (A.1.1) This exponential growth model had a profound effect on biology such as developing the theory of natural selection (Darwin 1859). -
Litter Pollution in Densely Versus Sparsely Populated Areas: Dog River Watershed
LITTER POLLUTION IN DENSELY VERSUS SPARSELY POPULATED AREAS: DOG RIVER WATERSHED Gabrielle M. Hudson, Department of Earth Sciences, University of South Alabama, Mobile, AL 36688. E-mail: [email protected]. It is commonly known that when humans populate an area that area is usually subject to some environmental degradation. One of the more common aspects of environmental degradation is litter. This type of degradation is no stranger to the Dog River watershed. For years residents have seen the rivers in this watershed covered in trash, specifically after rain events. The vast majority of the trash is a result of litter from roadsides being carried into the river via drainage pipes. This paper is a comparative study of litter in areas of varying population densities in the Dog River watershed. It seeks to distinguish between the amount of litter found in densely populated areas and sparsely populated areas, and to find out if there is a correlation between population density and litter. I utilize GIS to map population density of the Dog River watershed, and analyze and compare the amounts of litter in areas of sparse and dense populations. The results show that there is no correlation between population density and litter. It also shows that there is no difference in the amounts of litter found in densely and sparsely populated areas. Keyword: litter, population density, watershed Introduction Pollution has long been an issue in the Dog River watershed, in particular litter pollution. The extent of the pollution has not gone unnoticed. There are groups of people and organizations who have taken increased interest in the Dog River watershed with intentions of reducing pollution, including Dog River Clearwater Revival and its numerous volunteers. -
Population Growth & Resource Capacity
Population Growth & Resource Capacity Part 1 Population Projections Between 1950 and 2005, population growth in the U.S. has been nearly linear, as shown in figure 1. Figure 1 U.S. Population in Billions 0.4 0.3 0.2 Actual Growth 0.1 Linear Approximation - - - H L Year 1950 1960 1970 1980 1990 2000 2010 Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. World Population Prospects: The 2010 Revision. If you looked at population growth over a longer period of time, you would see that it is not actually linear. However, over the relatively short period of time above, the growth looks nearly linear. A statistical technique called linear regression can create a linear function that approximates the actual population growth over this period very well. It turns out that this function is P = 0.0024444t + 0.15914 where t represents the time variable measured in years since 1950 and P represents the (approximate) population of the U.S. measured in billions of people. (If you take statistics, you’ll probably learn how to obtain this function.) The graph of this linear function is shown in the figure above. (1) Just to make sure that you understand how to work with this function, use it to complete the following table. The actual population values are given. If you are working with the function correctly, the values you obtain should be close to the actual population values! Actual Population t P Year (billions of people) (years) (billions of people) 1960 .186158 1990 .256098 2005 .299846 (2) Use the linear function to determine the approximate year when the population of the U.S. -
Fisheries in Large Marine Ecosystems: Descriptions and Diagnoses
Fisheries in Large Marine Ecosystems: Descriptions and Diagnoses D. Pauly, J. Alder, S. Booth, W.W.L. Cheung, V. Christensen, C. Close, U.R. Sumaila, W. Swartz, A. Tavakolie, R. Watson, L. Wood and D. Zeller Abstract We present a rationale for the description and diagnosis of fisheries at the level of Large Marine Ecosystems (LMEs), which is relatively new, and encompasses a series of concepts and indicators different from those typically used to describe fisheries at the stock level. We then document how catch data, which are usually available on a smaller scale, are mapped by the Sea Around Us Project (see www.seaaroundus.org) on a worldwide grid of half-degree lat.-long. cells. The time series of catches thus obtained for over 180,000 half-degree cells can be regrouped on any larger scale, here that of LMEs. This yields catch time series by species (groups) and LME, which began in 1950 when the FAO started collecting global fisheries statistics, and ends in 2004 with the last update of these datasets. The catch data by species, multiplied by ex-vessel price data and then summed, yield the value of the fishery for each LME, here presented as time series by higher (i.e., commercial) groups. Also, these catch data can be used to evaluate the primary production required (PPR) to sustain fisheries catches. PPR, when related to observed primary production, provides another index for assessing the impact of the countries fishing in LMEs. The mean trophic level of species caught by fisheries (or ‘Marine Trophic Index’) is also used, in conjunction with a related indicator, the Fishing-in-Balance Index (FiB), to assess changes in the species composition of the fisheries in LMEs. -
Density Dependence in Demography and Dispersal Generates Fluctuating
Density dependence in demography and dispersal generates fluctuating invasion speeds Lauren L. Sullivana,1, Bingtuan Lib, Tom E. X. Millerc, Michael G. Neubertd, and Allison K. Shawa aDepartment of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN 55108; bDepartment of Mathematics, University of Louisville, Louisville, KY 40292; cDepartment of BioSciences, Program in Ecology and Evolutionary Biology, Rice University, Houston, TX 77005; and dBiology Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543 Edited by Alan Hastings, University of California, Davis, CA, and approved March 30, 2017 (received for review November 23, 2016) Density dependence plays an important role in population regu- is driven by reproduction and dispersal from high-density pop- lation and is known to generate temporal fluctuations in popu- ulations behind the invasion front (13–15)]. The conventional lation density. However, the ways in which density dependence wisdom of a long-term constant invasion speed is widely applied affects spatial population processes, such as species invasions, (16, 17). are less understood. Although classical ecological theory suggests In contrast to classic approaches that emphasize a long-term that invasions should advance at a constant speed, empirical work constant speed, there is growing empirical recognition that inva- is illuminating the highly variable nature of biological invasions, sion dynamics can be highly variable and idiosyncratic (18–25). which often exhibit nonconstant spreading speeds, even in sim- There are several theoretical explanations for fluctuations in ple, controlled settings. Here, we explore endogenous density invasion speed (which we define here as any persistent tem- dependence as a mechanism for inducing variability in biologi- poral variability in spreading speed), including stochasticity in cal invasions with a set of population models that incorporate either demography or dispersal (24–28) and temporal or spatial density dependence in demographic and dispersal parameters. -
Modeling Population Dynamics
Modeling Population Dynamics Gary C. White INTRODUCTION Population modeling is a tool used by wildlife managers. At its simplest, population modeling is a book keeping system to keep track of the four components of population change: births, deaths, immigration, and emigration. In mathematical symbolism, a population model can be expressed as Nt+1 = Nt + Bt - Dt + It - Et . Population size (N) at time t+1 is equal to the population size at time t plus births (B) minus deaths (D) plus immigrants (I) minus emigrants (E). The simplicity of the relationship belies its usefulness. If you were managing a business, you would be interested in knowing how much money was being paid out of the business, relative to how much money was coming in. The net difference represents your “profit”. The same is true for managing a wildlife population. By knowing the net change in the size of the wildlife population from one year to the next, you as a wildlife manager know whether the population is “profitable” (i.e., gaining in size), or headed towards bankruptcy (i.e., becoming smaller). Two examples will demonstrate typical uses of population models in big game management. In November 1995, voters in Colorado passed an amendment to the state’s constitution that outlawed hunting black bears during spring. Wildlife managers were asked if the bear population would increase because of no spring harvest. A population model can be used to answer this question. The second example of the use of a model is to determine which of two management schemes (e.g., no antlerless harvest versus significant antlerless harvest) will result in Modeling Population Dynamics – Draft May 6, 1998 2 the largest buck harvest in a mule deer population. -
Utah's Long-Term Demographic and Economic Projections Summary Principal Researchers: Pamela S
Research Brief July 2017 Utah's Long-Term Demographic and Economic Projections Summary Principal Researchers: Pamela S. Perlich, Mike Hollingshaus, Emily R. Harris, Juliette Tennert & Michael T. Hogue continue the existing trend of a slow decline. From Background 2015-2065, rates are projected to decline from 2.32 to 2.29. These rates are projected to remain higher The Kem C. Gardner Policy Institute prepares long-term than national rates that move from 1.87 to 1.86 over demographic and economic projections to support in- a similar period. formed decision making in the state. The Utah Legislature funds this research, which is done in collaboration with • In 2065, life expectancy in Utah is projected to be the Governor’s Office of Management and Budget, the -Of 86.3 for women and 85.2 for men. This is an increase fice of the Legislative Fiscal Analyst, the Utah Association of approximately 4 years for women and 6 years for of Governments, and other research entities. These 50- men. The sharper increase for men narrows the life year projections indicate continued population growth expectancy gap traditionally seen between the and illuminate a range of future dynamics and structural sexes. shifts for Utah. An initial set of products is available online • Natural increase (births minus deaths) is projected to at gardner.utah.edu. Additional research briefs, fact remain positive and account for two-thirds of the cu- sheets, web-enabled visualizations, and other products mulative population increase to 2065. However, giv- will be produced in the coming year. en increased life expectancy and declining fertility, the rate and amount of natural increase are project- State-Level Results ed to slowly decline over time. -
Immigration and the Stable Population Model
IMMIGRATION AND THE STABLE POPULATION MODEL Thomas J. Espenshade The Urban Institute, 2100 M Street, N. W., Washington, D.C. 20037, USA Leon F. Bouvier Population Reference Bureau, 1337 Connecticut Avenue, N. W., Washington, D.C. 20036, USA W. Brian Arthur International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria RR-82-29 August 1982 Reprinted from Demography, volume 19(1) (1982) INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS Laxenburg, Austria Research Reports, which record research conducted at IIASA, are independently reviewed before publication. However, the views and opinions they express are not necessarily those of the Institute or the National Member Organizations that support it. Reprinted with permission from Demography 19(1): 125-133, 1982. Copyright© 1982 Population Association of America. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage or retrieval system, without permission in writing from the copyright holder. iii FOREWORD For some years, IIASA has had a keen interest in problems of population dynamics and migration policy. In this paper, reprinted from Demography, Thomas Espenshade, Leon Bouvier, and Brian Arthur extend the traditional methods of stable population theory to populations with below-replacement fertility and a constant annual quota of in-migrants. They show that such a situation results in a stationary population and examine how its size and ethnic structure depend on both the fertility level and the migration quota. DEMOGRAPHY© Volume 19, Number 1 Februory 1982 IMMIGRATION AND THE STABLE POPULATION MODEL Thomos J. -
Plant Diversity Increases with the Strength of Negative Density Dependence at the Global Scale
RESEARCH FOREST ECOLOGY predators, pathogens, or herbivores) and/or com- petition for space and resources (2–4, 7). Numer- ous studies have documented the existence of CNDD in one or several plant species (8–12), and Plant diversity increases with the most of these studies explicitly or implicitly as- sume that stronger CNDD maintains higher spe- strength of negative density cies diversity in communities. However, only a handful of studies have explicitly examined dependence at the global scale the link between CNDD and species diversity (4, 11, 13, 14),andnostudyhasexaminedthis relationship across temperate and tropical lat- Joseph A. LaManna,1,2* Scott A. Mangan,2 Alfonso Alonso,3 Norman A. Bourg,4,5 itudes. Despite decades of study, our understand- Warren Y. Brockelman,6,7 Sarayudh Bunyavejchewin,8 Li-Wan Chang,9 ing of how processes at local scales—such as Jyh-Min Chiang,10 George B. Chuyong,11 Keith Clay,12 Richard Condit,13 density-dependent biotic interactions—influence Susan Cordell,14 Stuart J. Davies,15,16 Tucker J. Furniss,17 Christian P. Giardina,14 18 18 19,20 global patterns of biodiversity remains in flux I. A. U. Nimal Gunatilleke, C. V. Savitri Gunatilleke, Fangliang He, 1 15 21 22 23 ( , ). Robert W. Howe, Stephen P. Hubbell, Chang-Fu Hsieh, Both species-specific and more generalized 14 24 25 15,16 Faith M. Inman-Narahari, David Janík, Daniel J. Johnson, David Kenfack, mechanisms can cause CNDD, but only CNDD 3 24 26 17 Lisa Korte, Kamil Král, Andrew J. Larson, James A. Lutz, caused by species-specific mechanisms can main- 27,28 4 29 Sean M. -
"Density Dependence and Independence"
Density Dependence and Advanced article Independence Article Contents . Introduction: Concepts and Importance in Ecology Mark A Hixon, Oregon State University, Corvallis, Oregon, USA . Mechanisms of Density Dependence . Old Debates Resolved Darren W Johnson, Oregon State University, Corvallis, Oregon, USA . Detecting Density Dependence . Future Directions Online posting date: 15th December 2009 Density dependence occurs when the population growth parameter of interest involves population dynamics, rate, or constituent gain rates (e.g. birth and immi- including the population growth rate and the four primary gration) or loss rates (death and emigration), vary caus- demographic (or vital) rates – birth, death, immigration ally with population size or density (N). When these and emigration – although related parameters, such as growth and fecundity, are also investigated. See also: parameters do not vary with N, they are density-inde- Population Dynamics: Introduction pendent. Direct density dependence, where the popu- Use of the words ‘density dependence’ alone normally lation growth rate or gain rates vary as a negative means ‘direct density dependence’ (or compensation): the function of N, or the loss rates vary as a positive function of per capita (proportional) gain rate (population or indi- N, is necessary but not always sufficient for population vidual growth, fecundity, birth or immigration) decreases regulation. The opposite patterns, inverse density as N increases (Figure 1a) or the loss rate (death and/or dependence or the Allee effect, may push endangered emigration) increases as N increases (Figure 1b). The populations towards extinction. Direct density depend- opposite patterns are called inverse density dependence (or ence is caused by competition, and at times, predation.