Population Ecology & Biosystematics

Transcription

1 Population Ecology & Biosystematics Population: a group of conspecific individuals occupying a particular place at a particular time This is an operational definition Compare with Deme: a population unevenly distributed in space with real natural boundaries. A deme can be a subset of a population or an isolated or semi-isolated population. (a local population of closely related interbreeding species)

2 Population Features and Terms Abundance: number of individuals Density: number of individuals/unit area Natality: production of new individuals Mortality: loss of individuals due to death Emigration & Immigration: loss or gain to a population due to movement of individuals

3 Factors of Change in Abundance Birth Immigration Abundance Emigration Death

4 When high insect population escapes Balance of Nature OR the original habitat. then Immigrant population in a new habitat Normal tendency is that insects grow in Exponential fashion a measure of population growth (only natality). Math. expression dn/dt = rn

5 Exponential growth where r = intrinsic rate of population increase, N = starting number of the population. t = time hr, days, week, month, yrs, instars, etc. ie. rate at which population multiplies in this case only birth, no death, and habitat limitless. A state of uninhibited multiplication of the pests population explosion! Why? no natural agent to inhibit growth

6 Population Dynamics How a species is distributed and how it changes over time. populations can stay steady with time or, they can undergo exponential growth (a J-shaped curve, ). Ends up increasing very quickly, but the rate can vary. The doubling time of a population is the critical parameter: How long it takes to double the population size. or, they can grow exponentially for a while and then level off as limits are approached (logistic growth). carrying capacity : how many of a species can a given area support. If the population grows much past the carrying capacity, it will have a die off. limiting factors: food, waste disposal, predators, nesting/mating sites

7 Number Geometric/Exponential Population Growth Growth under ideal conditions Occurs in populations in early stages of growth Time N t rn N: change in number t: change in time r: per capita growth rate (birth death) N: size of population

8 Number Geometric Population Growth N t rn If r=0.2 and N=50, at the next time interval, N/ t=0.2(50)=10. Time So, N t+1 = 50+10=60 and N t+2 = 72. And so on, and so on, and so on

9 Exponential Growth (continuous model) Continuous model is equivalent to a discrete difference equation with an infinitely small time step. We treat time as being continuous so change in population size is described by a differential equation: dn/dt = B D = bn dn = (b d) N = rn where b and d are per capita birth and death rates. dn/dt = rn where r is the instantaneous rate of increase The units of r are individuals/(individuals * time) r > 0, exponential increase r = 0, no change stationary population r < 0, exponential decline NOTE: r is also called the intrinsic rate of increase.

10 How do we predict total population size at some particular time? We integrate the differential equation dn/dt = rn N t = N 0 e rt where e is Example: N 0 = 100, r = , t = 10 years N 10 = 100(e ) 10 = 405 individuals

11 The critical problem for models of exponential growth is Lack of realism Natural populations are limited by physical and biological features of their environments. These limiting factors prevent exponential growth from continuing for long periods of time. You have seen in Populus simulations a number of different patterns population growth may take. Real populations may show much more complicated patterns than the simple models display.

12 The herons are a useful example that limits to growth are usually evident. Population size varies, but never reaches or exceeds 5000 birds. Models that have a maximum population size, designated by K, also called the carrying capacity, are density-dependent, or logistic models. Here s what the growth curve looks like:

13 The model tells you what to expect about growth by comparing the current N with the value of K

14 The logistic model must account for the level of saturation of the environment with individuals in the population. For example, if K = 100, and there are currently 50 individuals in a population, we would say that the environment was half saturated with members of this population. (N/K = 50/100 = 50%) At N = 80, the environment is 80% saturated. More important than a value for saturation, as N grows closer to K, the rate of growth within this population should slow.

15 Logistic growth Another measure of growth is Logistic growth Will lead to population equilibrium and population oscillation in time Math. expression dn/dt = rn{(k-n)/k} where r = intrinsic rate of popn increase, N = starting number of the popn. t = time hr, days, week, mth, yrs, instars, etc. and K = carrying capacity, ie. limit of the habitat as governed by the biotic potentials, as in the preypredator relationship, food availability, competition for shelter, mate, etc.

16 Number Logistic Population Growth K Reality check modification of the geometric model Sets upper limit on population size N t rn K N N Time K: Carrying capacity. Maximum population size that can be sustained on an area

17 Number Logistic Population Growth K N t rn K N N If r=0.2, N t =90, and K=100, N/ t=0.2(90)(100-90/90) =(18)(10/90)=2, and N t+1 =90+2=92 Time Population grows by 2 vs. 18 individuals. If N exceeds K, growth becomes negative.

18 Other ways to express population growth In space no. per unit area OR unit volume In time mean annual growth rate (MAGR) (%) such as MAGR(%) = {(P2-P1) / (T2-T1)} x (100/P1) whereby P = population size T = time/yr Eg. If (T1) 1920 = (P1) 576,872 (T2) 1930 = (P2) 834,964 then {(834, ,872) / ( )} x (100 / 576,872) gets MAGR = 4.7%

19 Combined graph exponential logistic So, (K-N)/K is the unutilised opportunities for population. growth. Its impact becomes greater as the population grows larger.

20 Exponential Growth vs. Logistic Growth

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22 Population abundance/density can also be measured using Capture-mark-release-recapture method This technique is often used for population census in time and space, Population monitoring & surveillance for both pests and biocontrol agents for resurgence & outbreaks. How to avoid outbreaks or lower the pest population no.? ie. control! If Biological Controls were to be adopted in theory starts by reconstructing the Balance of Nature.

23 What aspect of NATURE? Biotic agents living entity predictable Abiotic agents unpredictable Predator-Prey Dynamics Functional & Numerical Responses Functional Responses the increase in the number of prey eaten per unit time and area per predator as the prey density increases. ie. it contributes to popn regulation when the proportion of the prey killed increases with the increase in prey density at a constant predator density predation rate. It determines to what extent and how much a predator is dependent on its prey or the predator s response to the primary prey density and this depends on the physical heterogeneity of the prey habitat such as availability & complexity of cover/shelter & alternate prey for the predator.

24 The relationship between the rate of attack by the predator and its prey density is represented by 3 forms of responses Based on the biomass eaten/predator/unit time 1. constant density-dependent. 2. inverse density-dependent. 3. positively density-dependent over a limited range of time.

25 Types of Fuctional Respones according to Holling (1959) Type I - constant density-dependent, a linear rise to a plateau (satiation) Type I functional response is found in passive predators like spiders. The number of flies caught in the net is proportional to fly density. Prey mortality due to predation is constant

26 Type II - negatively accelerated rise, increasing curve, leveling to a plateau Predation rate affected by foraging behaviour Biotic Genetic characteristic. Developmental stages Deprivation / difficulty level Prey size & prey density Abiotic Temperature, Plant architecture Humidity Crop habitat Light intensity

27 Type II functional response is most typical and corresponds to the equation above. Search rate is constant. Plateau represents predator saturation. Prey mortality declines with prey density. Predators of this type cause maximum mortality at low prey density. For example, small mammals destroy most of gypsy moth pupae in sparse populations of gypsy moth. However in highdensity defoliating populations, small mammals kill a negligible proportion of pupae.

28 Type III - positively density-dependent (delayed) over a limited range of time, a sigmoid increasing curve, there is perhaps the element of learning, giving an S-shaped rise to a plateau. Type III functional response occurs in predators which increase their search activity with increasing prey density. For example, many predators respond to kairomones (chemicals emitted by prey) and increase their activity. Polyphagous vertebrate predators (e.g., birds) can switch to the most abundant prey species by learning to recognize it visually. Mortality first increases with prey increasing density, and then declines.

29 Holling s disc equation : Ne = a Tt Nt / 1 + a Th Nt Ne = no. prey consumed per predator a = instantaneous rate (constant) of discovery/catch (prey/unit time, h) ie. a/tt where a = intercept of linear regression (catching freq. vs prey density) Tt = exposure time (unit time, eg. 24 h) Nt = total prey no. (density) Th = handling time (h/time), time to catch and consume the prey = b/a where b = slope of the linear regression

30 What response(s) does an insect predator display? What about FR for immature predator? What about in a multi-crop system? multi-predator species system? multi-hosts(pests) habitat system? early season? late season?

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32 Numerical Response (NR) The change in the number (increase) of predator caused by the increase in the prey density, ie. a measure of reproductive success of the predator in response to increase in prey no. as influenced by prey quality (can affect vigour of next generation), oviposition strategy of the predator, availability of prey source at time of predator emergence, distribution of the prey (affect rate of catch), presence/absence of alternative prey OR other predator species OR other stages of the predator (competition)... conducive ambient temperature

33 A positive reproductive response healthy colony If starts with a young prey colony predator must have good synchrony with prey.. so that can complete development before preys become scarce (depleted, migrate)

34 Uses of Life tables Projections of population estimates through several generations are made to identify the most vulnerable stage of the pest the key factors that cause mortality These facts in turn will allow us to estimate the degree of suppression required to keep the pest population below economic injury It aims at identifying the weakest link then can apply control input!

35 In the tropics food availability and refuge play crucial roles. Crop habitat and vegetative diversity provide significant impact. Will depict the number and fate of various stages of development during its life history. Then we will develop ideas on what, how & when to do to inflict additional mortality to the pest population

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37 Hypothetical life tables of insect populations 1st horizontal column = population in equilibrium. zero growth (rm = 0) 2nd horizontal column = population growing with high survival rate. A survival of a small percentage (eg. 2%) of the progeny can result in a destructive population meaning rm very +ve. Why? May be insecticides killed many predators! Could there be natural catastrophy drought! 3rd horizontal column = population exceeded equilibrium. Death rate > birth rate (rm -ve) Resulting in HT instead of DT.

38 Biosystematics Naming, identifying & classifying Biocontrol workers correct name, Disseminate information effectively Information such as reproductive biology, geographical distribution, host range, habits & growth requirements, etc. Cases of mistaken identity resulted many BC failures.. why? Wrong ID as exotic! search at wrong country of origin collect predators & parasitoids which are not effective, biogeographically unadoptable, etc. danger of establishing new pests, diseases danger of introduction hyperparasites, etc.

39 SOLUTIONS! establish networking Biological Control organisations more training & jobs for taxonomists