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Population Ecology (Distribution (Abiotic Factors that Limit Distributions…

- - - - Predation
        
        Restriction of Predators by Prey
        
        If predator is a specialized feeder (monophagous)
        
        Stress Gradient Hypothesis
        
        Abiotic conditions limit ranges when resource availability is low
        
        Unfavourable biophysical conditions prevail and negative biotic interactions (competition) limit ranges (less physiological stressful conditions)
        
        Predator control of Prey
        
        Prey survive outside the boundary of the local distribution of predators
        
        Predator and Prey abundances are Negatively correlated
        
        Putative Predator is able to exploit Prey in lab and field trials
        
        Putative Predator shown to be the cause of prey mortality in transplant experiments
      - Commensalism
      - Mutualism
      - Symbiosis
      - Parasitism
      - Competition
        
        Distribution
        
        Abundance
        
        Local Distributions - Relative abundance of species are negatively correlated (As one increases the other decreases vv)
        
        Checkerboard Distributions
        
        Competitive Exclusion
        
        Poorer competitor will be eliminated
        
        Poorer competitor selects resources that remove it from direct competition
        
        Evolutionary Adaptations
        
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        Food/Habitat Resources
  - - - Climate
        
        Complex Interactions Between
        
        Prevailing Wind Patterns
        
        Amount of Received Solar Radiation
        
        Differential Heating & Cooling Rates
        
        Altitude & Position Relative to Large Bodies of Water
        
        Local Climate
        
        Controlling Factors of Distribution
        
        Temperature
        
        Precipitation (Moisture)
        
        Options for Animals
        
        Evolve (physiologically/behaviourally)
        
        Move
      - Temperature Variation
        
        Difference between January and July mean temperatures increasing with Latitude and Inland Distance
    - - Impacts
        
        Species Assemblages
        
        Local Scale Distributional Effects
      - Temperature Variation
        
        Species Richness
      - Other Abiotic Variables (Precipitation)
        
        Runoff Volume and Patterns
    - - Determine the Life Stage most sensitive to the factor under study
      - Determine the physiological tolerance range for factor at that life-stage
      - Demonstrate the range over which the factor varies at sites where the species lives differ from that at sites which it does not live and is lethal at the non-distributed sites
    - - Common Garden Experiment
        
        Separates Phenotypic from Genotypic Variation
        
        Possible Outcomes
        
        Differences in expressed characteristics are phenotypic and ecotypes raised under identical conditions will respond similarly
        
        Differences in expressed characteristics are genotypic and ecotypes raised under identical conditions will remain
        when raised under identical conditions
        
        Differences in expressed characteristics are the product of phenotypic and genotypic differences and differences in expressed characteristics will be reduced, but not eliminated
  - - - Macro-Ecology
        
        Includes many habitat areas that a species does not occupy
        
        Scale Hierarchy
        
        Anderson proposed that species comprising the continental fauna for a given taxonomic group of organisms followed a logarithmic distribution
        
        A large number of species with small distributions
        
        A small number of species with large distributions
  - - - Move species to uninhabited area and tracking its reproduction and survival
        
        If successful
        
        Geography
        
        Dispersal Barriers
        
        No Dispersal Barriers
        
        Factors controlling Habitat Selection
        
        If unsuccessful
        
        Physiology
        
        Physical and Chemical environmental factors
        
        Growth
        
        Reproduction
        
        Survival
        
        Competition
        
        Can be eliminated if transplant experiment excludes predators/competition
  - - - Function of behavioural mechanisms (within the bounds of physiological performance capabilities)
    - - Adaptive reasons for habitat choice and the evolutionary significance of observed selection preferences
      - Evolution of Habitat Selection
        
        Certain habitats are preferred because they improve fitness
        
        Good habitat = higher fitness
        
        Poor habitat = lower fitness
        
        Selection preferences will vary by organism (general/specific)
        
        Habitat Gradient
        
        Pattern (non-random selection)
        
        Process (natural selection)
        
        Fretwell Model
        
        Model Assumptions
        
        Habitat occurs in patches (or along a gradient) that differs in quality relating directly to suitability (or fitness)
        
        Quality in any patch depends on the number of individuals using the patch (ie. is density-dependent)
        
        Individuals are free to move between patches
        
        Individuals act to maximize gains (fitness) from habitat occupancy
        
        Results
        
        "Ideal Free Distribution"
        
        Ideal in that individuals get the highest possible rewards available and free in that there are no constraints on movement
        
        Predicts that at high overall densities, good and poor habitats will be occupied
        Though at differing densities and at low overall densities individuals will tend to crowd into the best habitat
        
        Ideal Despotic Distribution
        
        Model Assumptions
        
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        Model Assumption Implications
        
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    - - Aim: to assess with measurement data which habitats are preferred in the sense that given choice, animals will use that habitat
      - Application: may be used to differentiate among habitats as a whole (e.g. woodland, grassland) or habitat attributes (shade, velocity, depth)
      - Approach
        
        Selection Ratio
        
        First and simplest means of distinguishing among habitats or diets (resources) in terms of preference
        
        wi = oi/pi
        
        wi > 1 indicates preference
        wi < 1 indicates avoidance
        
        Comparing Selection Ratios
        
        G-Test
        
        test of the selection ratios to determine whether the observed pattern deviates significantly from a random pattern
        
        Assumption: availability measures (pi) are measured without error (as would be in a complete census)
        
        Standardized Selection Ratio
        
        Compare selection ratios between study sites
        
        Bi > 1/n = preference
        
        Bi = wi/sum of wi
        
        Confidence Limits
        
        Estimate of standard error of the ratio (sewi)
        
        Only applies if a single CI is computed
        
        If more than one CI is computed and used in comparisons, the Bonferroni correction factor must be applied
        
        Involves adjusting the nominal chosen level of significance (alpha) for the number of intervals computed
        
        If 4 intervals are computed, the z(alpha) value used in the equation is divided by 4
        
        X2 test to determine if differences are statistically significant
    - - Experiences
      - Body Size (Micro-habitat selection)
      - Frequency of Use
      - Frequency of Availability
  - - - Simulations of Invasive Events
        
        Invasion successes decreases with the number of native species in the environment
        
        There is a priority effect for residents that is invasion by a superior competitor is difficult when the number of invaders is low
        
        Communities can shift among alternative predictable states as invasions/extinctions proceed
        
        Invasion resistance increases with the strength of community interactions (connectance) and species number
        Connectance: the ratio of actual food web inter-specific interactions between species compared with the total possible number
        
        Invasion resistance-connectance results holds across multiple trophic categories
        
        Invasions are facilitated where human disturbance is greatest (holds in freshwaters)
      - Conceptual Model - Stages
        
        Arrival
        
        Establishment
        
        Biotic Resistance
        
        Availability of prey, potential predators, parasites, disease, and competition
        
        Demographic Resistance
        
        Numbers introduced and ability to increase abundances when rare (r-selection)
        
        Environmental Resistance
        
        Integration
        
        Niche Shift or Displacement
        
        Functional changes to local ecology
      - Ten's Rule Model
        
        1% chance of species transition from one stage to the next
        
        Introduction
        
        Established
        
        Pest
        
        Exceptions
        
        Biological control insects where deliberate release in high numbers reduces effect of natural controls
        
        Island mammals, limited data outside of deliberate introductions suggest successes can be high
        
        Non-native crop plants where success is very high because plants strongly selected to grow where introduced (especially GMOs)
        
        Hawaiian birds where introduction success >50%, where success likely relates to destruction of habitats
      - Colonization & Extinction
        
        Occur in varying temporal and spatial scales
        
        Skewed Distribution
        
        most species and genera are short-lived
        
        most species have few individuals
        
        most genera have few species
        
        most species occupy small geographic ranges
        
        Population size important for reducing extinction risk
        
        Body size important for reducing extinction (secular scales)
    - - Diffusion
        
        Result of natural population growth
        
        Passive Diffusion
        
        Plants - disperse via wind, animals
        
        mean and maximum dispersal distances insufficient to explain re-colonization of northern latitudes following glacial retreat
        
        Reid's Paradox: discrepancy between theoretical and actual dispersal rates
        
        Active Diffusion
        
        Animals - driven by foraging or territory needs
        
        Species Limited by Climatic Factors
      - Secular Dispersal
        
        Occurs in evolutionary time, effect of natural selection with range expansion can cause migrants to diverge from ancestral population to a new species
      - Human Mediated Dispersal
        
        Intended
        
        Pest Control (agriculture)
        
        Accidental
        
        Solutions
        
        Prevention
        
        Early Detection
        
        Appropriate Control
    - - Physical
        
        Continental Drift
        
        Glaciation
      - Physiological
- - - - Ecological Methods
        
        Field
        
        Laboratory
        
        Theoretical
      - Ecological Inference Paradigm
        
        Manipulative Experiments
        
        Altered Conditions
        
        Observational Studies
        
        Gradient of Conditions
        
        Data Collection/Measurement #
        
        Hypothesis Validation/Falsification #
- - - - Plot Count (Quadrat) Method
        
        Critical Steps
        
        Determine size and shape of the area, taking into account behavioural, physical and sampling constraint information
        
        Establish number of plots in advance
        
        Locate plots randomly
        
        Plots may cover a standardized area (square, circle, rectangle - quadrats) or consist of non-overlapping strips (transects)
        
        A = area of interest
        a = area of quadrats
        N = number of plots/quadrats (A/a)
        n = total # of species captured
        s = # of quadrats sampled
        n(bar) = average # of animals in s (n/s)
        Nstar = (A/a) x n(bar) --> N/n(bar)
        Density = Nstar/A
      - Mark Recapture
        
        Critical Steps
        
        Single random sample of collected, marked and released
        
        an elapse of time sufficient for marked and unmarked to randomly re-mix
        
        A subsequent single random re-sample of the same population
        
        Assumptions
        
        Closed population
        
        Identical capture probability
        
        Marking does not affect subsequent catchability
        
        The second sample is random - marked and unmarked have an equal probability of capture
        
        Markings are not lost between sample periods
        
        All markings are reported on recovery in the second sample
        
        Sample may be biased in <16
        
        M = # caught and marked in sample 1
        C = number caught in sample 2
        R = # of marked recaptures in sample 2
        Nstar = estimated population size (Petersen Estimate)
        Nstar = MC/R
        
        Without Replacement
        
        Chapman's Modification
        
        With Replacement
        
        Bailey's Modification
        
        Confidence Limits
        
        R/C > 0.10
        
        Yes: Binomial CIs
        
        No
        
        R > 50
        
        Yes: Normal Approximations CIs
        
        No: Poissons CIs
      - Depletion Methods
        
        Assumptions
        
        Population is stationary during trapping (Intensive trapping over short time period usually sufficient to meet assumption)
        
        Capture probability is identical for each animal
        
        Capture probability remains constant from trapping to trapping
        
        Nstar = estimated population size
        N = true population size
        p = probability of capture during a single trapping
        q = (1 - p) probability of escaping capture during a single trapping
        k = number of trappings (removals)
        Ci = # of animals captured during ith trapping
        T = total # of animals captured in all passes
        Nstar = [6Z^2 - 3ZT - T^2 + Tsqrt(T^2 + 6ZT - 3Z^2)] / 18(2 - T)
        Z = 2xC1 + C2
        T = C1 + C2 + C3
        
        k = 3 is the minimum number of samples required for statistical tests of assumption validity
      - Census Methods
        
        Count all animals/plants in a given area
        
        Difficult with small species
        
        Rarely used - species with small populations and known, limited ranges
      - Sampling Methods
        
        Counting or marking and recapturing animals in a given area
        
        Most often used
        
        Requires careful field experimentation
    - - Examples of Types of Indices
        
        Counts over a period of time
        
        traps (catch per unit effort)
        
        vocalization frequency for birds
      - Caution
        
        Cannot be used to predict population abundance unless calibrated against actual abundance/density estimates
      - Natality
        
        B = natality
        Nn = # of new individuals
        t = time
        B = Nn/t
      - Mortality
        
        Chapman-Robson Method
        
        Disadvantages
        
        Heincke-Burgoyne Method
        
        Assumptions
        
        Reproduction is constant from year to year
        
        Survival is equal for all groups
        
        Survival is constant from year to year
        
        Imply that age composition can be used to estimate mortality because factors that affect age composition are constant
        
        Nt = N0e^-Zt
        Nt = # alive at time t
        N0 = # initially alive at t = 0
        Z = instantaneous total mortality
        t = time elapsed since t0
        
        Linear Regression
        ln(Nt) = ln(N0) - Zt
        
        Catch Curves
        
        ln (no harvested) decreases with age (not including juveniles)
        
        Slope of the estimated regression yields Z
        
        M = 1 - S
      - Immigration/Emigration
        
        Assumptions
  - - - Semelparous
    - - Iteroparous
- - - - Moves the Mean
    - - Selects against Extremes
      - Occurs in Stable Environments
      - Unknown Effects on the Mean
      - Reduces Trait Diversity/Variation from period to period
    - - Two Divergent Phenotypic Extremes
  - - - Mutation & Gene Flow
      - Immigration/Emigration
      - Environmental Change
      - Relatively Slow Rate of Change
      - Reactive to a Given Set of Conditions History of Previous Adaptations
  - - - Reproduction
        
        Darwinian Fitness
        
        Time-dependent
        
        Relative
        
        Applies only to individuals of the same species (living under identical conditions)
      - Survival