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Biol 457 - Final Exam Notes (Full Course).pdf

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Department
Biology
Course
BIOL 457
Professor
Kim Kuddington
Semester
Winter

Description
Lecture 1 – syllabus, definitions and history of community ecology, definitions of communities: extended example (coral reefs) What is ecology and why care about it?  Study of the interactions of whole organisms with the biotic and abiotic environment  Human Health and Medicine: o Disease, toxins, malnutrition  Agriculture and Natural Resources: o Forestry and fishery, pest management  Transfer of techniques to other disciplines: o Models of cultural change in sociology o Genetic algorithms in computer science  Environment and Conservation: o Bioremediation, effects of climate change, loss of biodiversity, fresh water supplies What is community ecology? The study of 2 or more interacting species Organizational Hierarchy:  sub-organism  whole organism  population  community  ecosystem  landscape  Complete separation and distinctions between the levels are artificial. You should understand one level above and below Big questions in community ecology  How do communities change in space/time?  Which species are common or rare, why?  What are communities diverse?  What determines community stability/resilience?  What happens as community diversity dramatically decreases with human activity? o What about the functions that the community is providing fur us? Approaches in community ecology  Observations  Experimental methods  Mathematical modeling  Statistical techniques What is a community and how do they interact? Clements: Organismic Concept  Closely integrated system with birth, growth, maturation, development and death (homeostasis, repair)  Species rise and fall with each other in growth and abundance Gleason: Individualistic Concept  Random assemblages of species that happen to have the same growth requirements (may or may not interact)  Species growth and abundance increase and fall at different rates. There is no clear pattern 4 major definitions of communities Taxonomic (Physiognomically defined)  Recognized by the conspicuous presence of dominant species  Ex) coral reefs o Reef building corals require clear, warm water o Corals are symbiotic relationship between an animal and a vegetable.  Dinoflagellate called zooxanthellae(plant) with coral polyp (animal) o Makes a mineral-based skeleton o Several polyps make up a colony and they all contain nematocysts (sting cells) o They are considered ecosystem engineers because they build huge structures that is the home for many organisms and diversity o Coral Bleaching: lose their zooxanthellae  Ex) Forests, Wetlands (need to look at species composition – 50% of plant community consists of upland plant species) Physical  Ex) lake Statistical  Selection of samples that group on a PCA Interactive  Pollinator-pollination network Ocean Acidification  Ocean absorbs ~25% of atmospheric CO2 and dissolves into sea water to form carbonic acid  Past 250 years, sea water acidity has increased by 30% o By 2060, seawater acidity could have increased by 120% (if we continue what we are doing now without any change, we will give up coral reefs) Consequences  Coral reefs are affected: organism’s ability to fix calcium carbonate is reduced  Biodiversity of many benthic communities will continue to decline  Reduces the amount of food we have because coral reefs provide habitats for much of the food we consume Lecture 2 – Taxonomically-defined communities, using dichotomous keys, using stats to identify communities Ontario wetland evaluation standards (OWES): wetland are defined by species composition of plant community Dichotomous Key: tool that allows user to determine the identity of species Sampling  Sample the total population then infer characteristics of population from sample  Needs to be unbiased, random  Normality of data needs to be consistent  When sampling, you may want to take more samples, go at different times to reduce standard error Descriptive Statistics Central Tendency  Single summary measure for one variable (mean, median, mode)  Need to consider scatter (every set of samples will giveyou different estimate of mean) Dispersion  Measure of spread or variability between samples (variance, standard deviation)  Confidence limits: interval estimate for the population mean at a certain confidence that the real population mean is within the max and min  For large samples, the confidence interval can be calc by: Lecture 3 – using stats to identify communities-memories of the t-test  The t distribution changes shape based on number of samples. If you have little samples, it accounts for it (<30 samples)  The curve of the t-distribution is flatter  Degrees of freedom: error adjustment based on number of samples Test Hypotheses: The Five Step Model 1. Make assumptions and meet test requirements  Randomly sampled (different from haphazard where you just grab what you see)  Unbiased and precise  Sample distribution is normal in shape (proportional data is not normal)  To increase normality, (arcsin or square root of data) 2. State the null hypothesis  Null hypothesis and alternate hypothesis. Usually, the null means that the difference between the sample mean and population mean is attributed to random chance  (no significant difference between population and sample mean) 3. Select the sampling distribution and establish the critical region  Normal or t distribution  Critical region: 0.05 degrees of freedom = N – 1  Two-tailed test?  Direction of difference not predicted  Critical region split equally on both sides  One-tailed test? 4. Compute the test statistic   use formula to compute test statistic 5. Make a decision and interpret results  If value falls within critical region, reject null hypothesis. Otherwise, fail to reject. Lecture 4 – physically defined communities, niche space, climate and species richness Physically Defined Communities  Groups of populations that occur in the same physical area or same range of physical conditions  Ex) abiotically defined habitats  Ex) Pitcher plant o Liquid at bottom of pitcher and enzymes makes an aquatic community. o Has a very distinct boundary. Can find dead bugs in them (carnivorous plant)  Phytotelmata: aquatic environment created by plant structure  Ex) Tropical Rainforest: has rain and distinct set temperature, soils poor and thin  Ex) Desert: extremely low organic matter in soil, temperature always exceeds water so evaporation > precipitation o Deserts are found at 30 degree N and S of the equator because of Hadley cells  These cells are created from wind currents in 30 degree increments of warm air rising, cooling and falling. By the time it hits 30 degrees, no water is left to precipitate = solar driven air circulation  Ex) Terrestrial Biomes: can be distinguished by their climates, and also their predominant plants  Niche: fundamental niche: combination of conditions and resources that allows species to survive, grow and reproduce, in the absence of others o N-dimensional hypervolume: there are n axes of requirements for any number of species and the niche is the volume defined by the axes. Climate  Climate Diagrams: Summarize climatic information using a standardized structure o Temperature vs. precipitation for example  West coast climate is different from east coast because of the rain shadow effect  The Coriolis Effect: wind pattern due to the deflection of air caused by the rotation of the earth rotating at different speeds at different latitudes Species Richness  Species Richness: # of species. In general, higher as you decrease in latitude Hypothesis for why higher species richness at lower latitudes:  Greater productivity o More solar radiation & year-round growing season = greater photosynthetic activity  more species o Problem: Some studies show species richness highest at intermediate levels of productivity  Greater evolutionary age o Tropics not covered by glaciation = longer evolutionaryhistory  more species o Problem: pollen cores suggest major contraction of tropical forests during ice ages  Middle Domain Effect o “null hypothesis”. If species are distributed randomly in a bounded domain, more ranges will overlap in the middle than at the edges o Problem: doesn’t work when you test on islands  Less harsh/seasonal  Predation  Evolutionary Null Model  Null model: pattern-generating that is based on randomization of ecological data o Certain elements are held constant and others are allowed to vary Lecture 5 – statistically defined communities, using chi square tests, resemblance measures Statistically Defined Communities  Statistically Defined Communities: groups of species with a significant correlation in space/time  Species Association: recurring group of species that are “significantly” found together (doesn’t have to have an positive interaction among them)  Species distributions may be correlated in space or time because: o They have the same environmental requirements or because of ecological interactions Identifying Associations  Presence-absence or abundance data o Correlation analysis o Contingency table analysis o Other methods Chi Square Tests  Steps to test null hypothesis regarding co-occurrence 1. Organize in two-way contingency table 2. Calculate expected = (row total * column total) / sum total 3. Calculate test statistic a) Null Hypothesis: There is no diff b/w our observations and the expectation based on random distribution (species are probably not associated or disassociated) b) Alt Hypothesis: There is a difference… (species are probably associated or disassociated) 4. Determine significance a) Degrees of Freedom = (# rows – 1)(# cols – 1) Resemblance Measures  Resemblance measures: overall measure of the relationship among objects or attributes  Starting point for multivariate analyses  Similarity or Dissimilarity from shared presences and shared absences o In most cases, Dissimilarity = 1 – Similarity (does not work for indexes without an upper bound) Simple Matching Coefficient  Presences and absences contribute to similarity Binary Similarity Coefficients  Either asymmetric or symmetric Jaccard Binary Coefficient  Shared species as a proportion of total # species  Ignores shared absences  Ranges from 0 – 1 SƟrensen Coefficient  Ignores shared absences  Gives more weight to shared species Problems with Binary Similarity Coefficients  Small number of species rarely happens in real world  Same weight to rare and common species Abundance Data  Density, Frequency or Cover  %converting to percentage makes data add to 100 Quantitative Similarity Coefficients Percentage Similarity (Renkonen index)  Xij abundance of species I in the jth sampling unit  Xik abundance of species I in the kth sampling unit Quantitative Dissimilarity Measures  Both Euclidian and Manhattan Distances are zero for identical sampling units  Neither has a fixed upper bound Euclidian Distance  dissimilarity can be found by determining the distance between two points (rise/run) o this equation is similar to the Pythagorean theorem Manhattan (City Block) Distance  using a city block distance (straight lines x and y addition)  dissimilarity is found from the sum of differences Lecture 6 – Cluster Analysis, ordination  The above resemblance measures don’t work the same for more than two communities…  Instead, need a cluster analysis Cluster Analysis Similarity Matrix:  Take a Percentage Abundance Table  Convert to similarity matrix by calculating similarities between each two site pairs 2 different methods of Visual Analysis:  Cluster analysis = discrete o Put communities into discrete categories o Hierarchical classification: groups nested within other groups  Divisive Method: divides entire set of samples into smaller and smaller groups  Agglomerative method: starts with small groups and groups them into larger and larger clusters until entire data set is sampled  Ordination = continuous o “these sampling units are not similar” Single Linkage Cluster Analysis  Hierarchical, creates a matrix of similarity Steps: st 1. Find the most similar pair of samples = 1 cluster 2. Find whichever below is greatest: b) Second most similar pair of samples OR c) Highest similarity between a cluster and a sample OR d) Most similar pair of clusters 3. Continue until you have one large cluster 4. *the scale at the top represents the actual value for similarity Definitions:  Similarity between a sample and a cluster is the similarity between sample and the nearest member of the cluster  Similarity between two clusters is the similarity between the nearest member of each cluster Ordination  Simplifiesmultivariate data so we can analyze it by producing a multidimensional (1-3) picture  Distance = similarity, like the cluster analysis  Respresent 2D line with species 1 and species 2 in 1D by translating distances from the line of best fit Types of Ordination  Indirect Gradient Analysis: only species data used o Determine what the important gradients are from the species to create new variables  Direct Gradient Analysis: species data and environment data used Lecture 7 – Indirect ordination, direct ordination, quick population dynamics review Multivariate Analysis: analyze more than one or two variables  Analysis of Dependence: one or more variables are dependent, to be explained by others  Analysis of Interdependence: no variables dependent, look at relationships among variables Indirect Gradient Analyses  Uses species data only to determine similarity  circles superimposed are from cluster analysis showing some kind of cut off.  Distances of axes are not important  Axis 1 is more important than axis 2  Can have more than 2 axes Direct Gradient Analyses  Uses environmental data in addition to species data  It is a regression technique  Basically, more axes are added that correspond to environmental data Bray-Curtis (polar) Ordination Steps  Find dissimilarity matrix  Axis 1: o identify two extremes – most different (poles) o locate all other samples with respect to these two references samples  Axis 2: o Select another set of reference points (preferably ones that are very similar in first pole) o Locate all other samples with respect to these samples  Repeat this for all subsequent axes  Plot the points based on their distances from each other on first axis then on second axis Calculations  triangulation  Z is the sample you want.  The distance from A to Z is equal to x Metrics to describe species richness and diversity  Species Richness: usually based on the number of different species  Species Evenness: usually based on the consistency of species Rank-Abundance Curves Steps 1. Count the numbers of each species within a defined area 2. Calculate the frequency of each species 3. Plot the species frequencies as a function of frequency rank 4. The most even area is the one with the lowest slope (horizontal line) Review: Population Ecology  Population: a group of same species mostly in the same area  Populationt+1Population +tBirths + Immigration – Deaths – Emigration  Basic Closed Population model = no immigration or emigration Geometric Growth  λ = average number of offspring left by an individual during one time interval  t = number of time intervals in hours, days, years, etc. Exponential Growth  dN/dt = rate of population change  max = intrinsic rate of increase  N = number of individuals  Above is for determining population size anytime in the future Limits on exponential growth model  Assuming density-independent growth – highly unlikely Logistic Population Growth  Intraspecific competition: trying to meet requirements for resources, which are limited o Causes per capita population rates to decrease with an increase in density  K = carrying capacity: population density at which per capita growth rate is zero o Causes population growth to hit a population isocline where rate of change is zero Lecture 8 – Diversity metrics, Interactively-defined communities, methods of determining interactions Diversity Metrics  Combines richness and evenness in a single number. More equitable is considered more diverse Shannon Weiner Index th  Pi= proportion of the i species  Sum of the log of proportion times the proportion for each species  Higher value = more even = the greater the probabilitythat the next individual chosen within the community will not belong to the same species as the previous one  Note that if H = H it doesn’t necessarily mean they have the same richness and evenness. 1 2 Some information can be lost using a single number to describe these factors Interactively Defined Communities/Methods of Determining Interactions  Subset of species in a particular place whose interactions influences their abundance  Dorado examined how much rare species interact with other species in pollination  You can study interactions by direct observation, and sample at the same time and location o Advantages: interaction is directly observed and you can identify individuals o Disadvantages: time and money, only gives a snapshot of interaction  Ex) Kelp forest is interactively defined by kelps, sea otters and urchins Lecture 9 – Gut-content analysis, stable isotope analysis Gut-Content Analysis  Detect prey DNA in predator gut using PCR  Monoclonal antibodies react with antigen of target prey  GC-MS of prey alkaloids o Advantages: Cheap to process, can identify individuals o Disadvantages: need a large sample size, only gives a snapshot of diet. Very limited window to perform gut analysis Stable Isotope Analysis  Used to track elemental cycling and energy flow pathways  Useful in food web studies Stable isotopic fractionation  Alteration of the distribution of stable isotopes  Organisms have a different ratio of stable isotopes o Environment: 10% heavy, 90% light o Organism: 30% heavy, 70% light  Heavier isotopes take more energy to use. That is why organisms store more heavy isotopes and incorporate into tissues  **Isotope ratio of particular predator tissue becomes heavier than the prey items o This allows one to rank from primary producers to top predators  Mass Spectrometer uses electrical charge and magnets to measure deflection and this gives a related mass  If the value obtained from the above formula is greater than that for another organism, the one with the greater value is higher in trophic level o Advantages: relatively fast and easy processing, small sample size, long term view of diet o Disadvantages: expensive, no species identification (only isotope) Lecture 10 – Intro to Lokta-Volterra competition (pairwise species interactions) Intro to Pairwise Species Interactions  Competition, predation, mutualism, commensalism, amensalism Lokta-Volterra Competition Model  α 12the effect of species 2 on the rate of population growth of species 1 – usually in terms of resource use  plot each of these lines as isoclines (rate of change is zero) Lecture 11 – Lokta-Volterra competition, mechanisms of competition Lokta-Volterra Competition  stable: a stable equilibrium is one where it is approached and will stay there indefinitely  unstable: an unstable equilibrium is one where a little disturbance will move the population away from equilibrium  There are 4 possible arrangements of the isoclines in this way: o Species 1 wins o Species 2 wins o Winner depends on initial density o Species coexist Mechanisms of Competition Types:  Basic: o Resource Competition (organisms utilize common resources in short supply) o Interference Competition (organisms seek resources that may or may not be in short supply and harm each other in doing so)  Schoener: o Consumption o Pre-emption (being in a place first where resources might come) o Overgrowth o Chemical Interactions o Territoriality  EX) argentine ant wages war if there is another species of ants o Encounter competition Lecture 12 – Mechanisms of Competition ctd., Mechanistic Model of Resource Competition Mechanisms of Competition Allelopathy  Allelopathy: form of interference competition in which individuals of one species release toxins that harm other species  Ex) spotted knapweed. An invasive plant in north America has been very successful o Releases catechin which reduces germination and growth of native grasses o Cattle do not eat spotted knapweed so gives it another advantage Mechanistic Competition Models  Express competition coefficients and carrying capacities as rates of utilization/renewal of resources  In lokta volterra, we didn’t specific how competition was occurring  In mechanistic models, we use difference in the ability of species to grow at various levels of two or more resources to predict condition under which species will coexist Resource vs. Condition Pos. effect on per capita growth rate Pos. or neg. effect on per capita growth rate Can be used up Cannot be used up e.g. nitrogen, seeds e.g. sunlight, temperature Monad Model of Resource Use  designed for chemostat based on feed into chamber and effluent  reaches an asymptote  *do not need to memorize this equation  1/Y = how many resources one needs to eat to produce an offspring  R = resource  R* = equilibrium resource where reproduction and mortality are balanced  Horizontal line (m) is the level that balances with growth rate in order to have positive growth rate  Species 2 will win because it will eat the resources down to a level too low for species 1 to have positive population growth (lower than m1) Liebig  Liebig’s law of the minimum: where species require several resources to grow, growth rate will be determined by the resource in shortest supply  Essential vs. Substitutable resources  Growth of one species on two resources, there will only be positive growth when both resource minimums are satisfied (in dark green area) Tilman’s Mechanistic Approach  What if two species use more than one resources and use it at different rates?  two species on two resources  In region 2, only A survives because it is below the resource threshold for B to survive  But what happens in the question mark area? Not as simple as coexistence  The consumption vectors translated gives a better idea of what happens in the question mark area.  *Depends on slope of consumption vectors for each species (how fast each species consumes the resources)  Each species consumes more of the resource that limits itself  In this version with the slopes reversed, it is an unstable equilibrium  Each species uses more of the resource that limits the other species  Outcome depends on initial conditions Lecture 13 – Evidence for Competition, Experimental Design How do we know if competition is occurring?  Evidence for interspecific competition o Observed patterns consistent with predictions o Species overla
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