Stano Pekár"Populační ekologie živočichů" dN = Nr dt Té ma Datum 1 Adaptácia, fitness a fenotypova platicita 5.10. 2 Evolúcia pohlavia, determinácia pohlavia 5.10. 3 Početnosť a cykly 26.10. 4 Koncepcia r- a K- selekcie 26.10. 5 Geografická variabilita, teplota a klimatické zmeny 2.11. 6 Management ohrozených a invazívnych druhov 2.11. 7 Vnútrodruhová konkurencia 9.11. 8 Kooperácia a Alleeho efekt 9.11. 9 Medzidruhová konkurencia a princíp konkurenčného vylúčenia 16.11. 10 Nika a koexistencia 16.11. 11 Amensalizmus, komensalizmus a mutualizmus 23.11. 12 Posun znaku a konkurenčné uvolnenie 23.11. 13 Obrana pred predátormi 30.11. 14 Herbivóri/paraziti a ochrana rastlin/hostiteľov pred nimi 30.11. 15 Regulácia škodcov, lov a zber 7.12. 16 Teória optimálneho získavania potravy a teorém medznej hodnoty 7.12. 17 Sukcesia 14.12. 18 Pohyb v priestore, migrácia 14.12. Té ma 1 Adaptation, fitnes s and phenotype platicity 2 Evolution of sex, sex determination 3 Abundance and cykles 4 Koncept of r- and K- selection 5 Geografic variability, temperature and climatic changes 6 Management of endangered and invas ive species 7 Intras pecific competition 8 Cooperation and Allee efect 9 Inters pecific competition and the competitive exclus ion principle 10 Niche and coexis tence 11 Amensalis m, comensalis m and mutualis m 12 Character displacement and competitive releas e 13 Defence agains predators 14 Herbivores/paras ites and defence of plants/hos ts 15 Regulation of pes ts and harvesting 16 Optimal foraging and the marginal value theorem 17 Succes ion 18 Movement in s pace and migration Tkadlec E. 2009. Populační ekologie. Struktura, růst a dynamika populací. Univerzita Palackého. Begon M., Harper J.L. & Townsend R.T. 1997. Ekologie: jedinci, populace a společenstva. Univerzita Palackého. Jarošík V. 2005. Růst a regulace populací. Academia. Akcakaya H.R., Burgman M.A. & Ginzburg L.R. 1999. Applied Population Ecology. Principles and Computer Exercises using RAMAS EcoLab. Sinauer. Alstad D. 2001. Basic POPULUS Models of Ecology. Prentice Hall. Begon M., Mortimer M. & Thompson D.J. 1996. Population Ecology: A unified study of animals and plants. Blackwell. Bernstein R. 2003. Population Ecology. An Introduction o Computer Simulations. Wiley. Gotelli N.J. 2001. A Primer of Ecology. Sinauer. Hastings A. 1997. Population Biology. Concepts and models. Springer. Neal D. 2006. Introduction to Population Biology. Cambridge University Press. Ranta E., Lundberg P. & Kaitala V. 2006. Ecology of Populations. Cambridge. Shultz S.M., Dunham A.E., Root K.V., Soucy S.L., Carroll S.D. & Ginzburg L.R. 1999. Conservation Biology with RAMAS EcoLab. Sinauer. Stevens M.H.H. 2009. A Primer of Ecology with R. Springer. Vandermeer J.H. & Goldberg D.E. 2003. Population Ecology: First principles. Princeton. a major sub-field of ecology which deals with description and the dynamics of populations within species, and the interactions of populations with environmental factors expanding field (Price & Hunter 1995): - populations 52 %, communities 9 %, ecosystems 10 % main focus on - Demography = description of populations that gave rise to Life-history theory - Population dynamics = describe the change in the numbers of individuals in a population populations of member species may show a range of dynamic patterns in time and space central question: "WHAT DOES REGULATE POPULATIONS?" Change in abundance of Lynx and Lepus in Canada density independent factors, food supply, intraspecific competition, interspecific competition, predators, parasites, diseases 1. Conservation biology World Conservation Union (IUCN) uses several criterions (population size, generation length, population decline, fragmentation, fluctuation) to assess species status by means of Population viability analysis (PVA) estimates the extinction probability of a taxon based on known life history, habitat requirements, threats and any specified management options Saiga tatarica critical: 50% probability of extinction within 5 years endangered: 20% probability of extinction within 20 years vulnerable: 10% probability of extinction within 100 years 2. Biological control to assess ability of a natural enemy to control a pest in 1880 Icerya purchasi was causing infestations so severe in California citrus groves that growers were burning their trees in winter 1888-1889 Rodolia cardinalis and Cryptochaetum were introduced into California from Australia, growers took the initiative and applied the natural enemies themselves by fall 1889 the pest was completely controlled Rodolia cardinalis has been exported to many other parts of the world the interest of growers and the public in this project was due to its spectacular success: the pest itself was showy and its damage was obvious and critical; the destruction of the pest and the recovery of the trees was evident within months Rodolia cardinalis (Coccinellidae) eating Icerya purchasi (Hemiptera) 3. Epidemiology to predict the diffusion of a disease and to plan a vaccination phocine distemper virus was identified in 1988 and caused death of 18 000 common seals in Europe during 4 months the disease travelled from Denmark to the UK the population of common seals in the UK declined by about half 0 50 100 150 200 250 300 350 400 0 2 4 6 8 10 12 14 16 18 20 we e k no.ofdeadseals obs erved predicted Observed and predicted epidemic curves for virus in common seals in the UK Grenfell et al. (1992) 4. Harvesting to predict maximum sustainable harvest in fisheries and forestry but also used to regulate whale or elephant hunting when population is growing most rapidly (K/2) then part of population can be harvested without causing extinction density is determined by means of fitting logistic curves to data Beddington (1979) Relationship between capture and fishing effort Panulirus cygnus Population + environment = population system population conditions resources enemies molecules organels cells tissues organs organ systems organisms populations communities ecosystem landscape biosphere a group of organisms of the same species that occupies a particular area at the same time and is characterised by an average characteristic (e.g., mortality) characteristics: Individual Population Stage structure Age/stage structure Sex ratio Spatial distribution Size structure Developmental stage Age/stage Sex Territorial behaviour Size Event ­ an identifiable change in a population Process ­ a series of identical events * rate of a process ­ number of events per unit time Natality (,,birth rate") Mortality (,,mortality rate") Growth Population increase ("rate of increase") Consumption ("consumption rate") Birth Death Increment [gram] Increment [number] Acquisition of food [gram] ProcessEvent inherent characteristics of the evironment (pH, salinity, temperature, moisture, wind speed, etc.) not modified by populations not consumed by population no feedback mechanisms do not regulate population size limit population size optimal suboptimal unfavourable reproduction growth survival performance conditions any entity whose quantity is reduced (food, space, water, minerals, oxygen, sun radiation, etc.) modified (reduced) by populations defended by individuals (interference competition) regulate population size non-renewable resources - space Renewable resources - regeneration centre outside the population system no effect of the consumer (e.g., oxygen, water) - regeneration centre inside of the population system influenced by the consumer (e.g., prey) competitors, predators, parasites, pathogens negative effect on the population top-down regulation of the population Stano Pekár"Populační ekologie živočichů" dN = Nr dt focus on rates of population processes number of cockroaches in a living room increases: - influx of cockroaches from adjoining rooms immigration [I] - cockroaches were born birth [B] number of cockroaches declines: - dispersal of cockroaches emigration [E] - cockroaches died death [D] population increases if I + B > E + D rate of increase is a summary of all events (I + B - E - D) growth models are based on B and D spatial models are based on I and E Blatta orientalisEDBINN tt --++=+1 aim: to simulate (predict) what can happen models are tested by comparison with observed dynamic realistic models - complex (many parameters), realistic, used to simulate real situations strategic models - simple (few parameters), unrealistic, used for understanding of model behaviour a model should be: 1. a satisfactory description of diverse systems 2. an aid to enlighten aspects of population dynamics 3. a system that can be incorporated into more complex models deterministic models - everything is predictable stochastic models - including random events, chaos discrete models: - time is composed of discrete intervals or measured in generations - used for populations with synchronised reproduction (annual species) - modelled by difference equations continuous models: - time is continual (very short intervals) thus change is instantaneous - used for populations with asynchronous and continuous overlapping reproduction - modelled by differential equations STABILITY stable equilibrium is a state (population density) to which a population will move after a perturbation stable equilibrium unstable equilibrium Assumptions: immigration and emigration are ignored all individuals are identical reproduction is asexual resources are infinite for population with discrete generations (annual reproduction) if births and deaths do not depend on population size exponential (geometric) growth Malthus (1834) realised that any species can potentially increase in numbers according to a geometric series N0 = initial density b .. birth rate (per capita) d .. death rate Discrete (difference) model 1-= tt NN 11 -- -= tt dNbNN 11 )( -- -=- ttt NdbNN 1)1( --+= tt NdbN =-+ db1 < 1 .. population declines > 1 .. population increases = 1 .. population does not change time0 Nt t t NN 0= 012 NNN == population number in generations t is equal to number of individuals is multiplied each time - the larger the population the larger the increase = finite growth-rate, per capita rate of growth = 1.23 .. 23% increase R ..average of finite growth rates t t tt i iR 1 21 1 1 )...( = = = Comparison of discrete and continuous generations populations that are continuously reproducing when change in population number is permanent Nt time Continuous (differential) model t t NN 0= )ln()ln()ln( 0 tNNt += )ln()ln()ln( 0 tNNt =- )ln( 1 = Ndt dN )ln(N dt dN = Nr dt dN =)ln(=r Solution of the differential equation: - analytical or numerical at each point it is possible to determine the rate of change by differentiation (slope of the tangent) when t is large approximated by the exponential function r < 0 .. population declines r > 0 .. population increases r = 0 .. population does not change time N r - intrinsic rate of natural increase, instantaneous per capita growth rate Nr dt dN = r Ndt dN = 1 = TT rdtdN N 00 1 )0()ln()ln( 0 -=- TrNNT rT N NT = 0 ln rt t eNN 0= rTT e N N = 0 t t NN 0= rt t eNN 0= rtt e= )ln(=r r versus r is symmetric around 0, is not r = 0.5 ... = 1.65 r = -0.5 ... = 0.61 doubling time: time required for a population to double r t )2ln( = Demography - study of organisms with special attention to stage or age structure processes associated with age, stage or size x .. age/stage/size category px .. age/stage/size specific survival mx .. reproductive rate (expected average number of offspring per female) x x x S S p 1+ = x x x x m 0 = main focus on births and deaths immigration & emigration is ignored no adult survive one (not overlapping) generation per year egg pods over-winter despite high fecundity they just replace themselves Chorthippus Richards & Waloff (1954) Annual speciesAnnual species breed at discrete periods no overlapping generations BBiennaliennal speciesspecies breed at discrete periods adult generation may overlap adults adults 0 birth t0 t1 adults pre-adults 0 birth t0 t1 adults t2 p pre-adults birth t0 t1adults t2 0 pre-adults p breed at discrete periods breeding adults consist of individuals of various ages (1-5 years) adults of different generations are equivalent overlapping generations PerennialPerennial speciesspecies Parus major Perins (1965) age/stage classification is based on developmental time size may be more appropriate than age (fish, sedentery animals) Hughes (1984) used combination of age/stage and size for the description of coral growth Age-size-stage life-tableAge-size-stage life-table Agaricia agaricites