Biology 5868 Effects on Populations
Definitions of a “Population” • Newman & Unger – a group of individuals of the same species occupying a defined space at a particular time. • Robert Leo Smith (WV University) – a group of interbreeding organisms of the same kind occupying a particular space. • World Book Dictionary (biological) ­ the aggregate of organisms that inhabit a particular locality or region. Deme ~ Demos, which is Greek for country district and its people; a commune.
An Approach to Study Population Effects 1. Assume Toxicity ~ Disease Apply Epidemiology – a science focusing on the cause, incidence prevalence and distribution of diseases in human populations. 2. Tweak Epidemiology Environmental – sub discipline of human epidemiology; it focuses on chemical or physical agents. Ecological – morphed to fit ecological risk assessment; it focuses on methods to determine cause, incidence, prevalence and distribution of adverse effects to non­human species. Etiology – correlates adverse effects to risk factors such as individual characteristics (age) or etiological agents (contaminants) responsible for causing, initiating or promoting adverse effects.
Epidemiology Metrics •Incidence Rate (I) – number of individuals (N) observed with adverse effects per total time (T) at risk. Tells how fast adverse effects are realized. I = N / T •Prevalence (P) – is simply the Incidence Rate (I) multiplied by the length of time (t) at risk. Indicates extent that adverse effects are realized. P = I * t •Relative Risk (RR) – compares risks between populations. RR = I E (exposed) ∕ I C (control) RR > 1 indicates ↑ risk to the exposed population.
Shortcoming of Epidemiology RELIANCE on INFERENCE • Epidemiology is based on inferentially weak correlations (associations) of adverse effects vs. risk factors. Cause/Effect relationships are extremely difficult to identify and support at the population level. • Logical rules have been developed to enhance the inferential soundness of Epidemiology. Hill (1965) ­ Nine Aspects of Noninfectious Disease Development Fox (1991)­ Rules of Practical Causal Inference
Overview of Hill’s Logical Rules 1. Strength of Association Example: A 150 fold higher incidence vs. a 300 fold higher incidence of crossed­billed gulls in the Great Lakes (a clearly stronger association). 2. Consistency of Association Example: A 300 fold higher incidence of crossed bills was evident regardless of age or sex in gulls of the Great Lakes (greater consistency).
Overview of Hill’s Logical Rules 3. Specificity of Association Example: A 300 fold higher incidence of crossed bills was evident in gulls inhabiting industrialized bays of the Great Lakes (enhanced specificity). 4. Temporal Association Example: Analysis of historical information showed initial observations of crossed­bills to be preceded by known expansions of industrial activity (effects preceded by promoter).
Overview of Hill’s Logical Rules 5. Biological Gradient Example: Bay­specific frequencies of crossed bills tended to increase with bay­specific extents of industrialization (implied dose response). 6. Biological Plausibility Example: Physiological studies have shown that industrial chemicals influence Ca modulation in vertebrates (mechanism for association).
Overview of Hill’s Logical Rules 7. Coherence of Association Example: Lab assays show sublethal doses of industrial chemicals to be associated with abnormal jaw­bone development in rats (abnormal rat jaw­bones → crossed gull bills). 8. Experimental Support of Association Example: Industrialized bays experiencing manufacturing facility closures show decreased observations of gulls with crossed bills (indirect evidence of ↓ dose → + response).
Overview of Hill’s Logical Rules 9. Analogy Example: Anatomical abnormalities have been observed in non­human mammalian species inhabiting industrialized areas (establish similarity to a well­documented association). SO WHY THE EMPHASIS ON EPIDEMIOLGY?
BECAUSE 1. Traditional approaches using ANOVA­derived values (NOEC<MATC<LOEC) assume that statistical significance dictates biological significance. 2. Traditional approaches do not directly answer the question of whether or not the population will remain viable, despite the presence of a toxicant. For example, if toxicant produces a 10% decrease in a quality (say eyesight) that effect may be catastrophic to some populations (hawks) but not others (bats). These problems are somewhat ameliorated by Demographic Analysis (based on Epidemiology).
General Population Response Simplest models treat all individuals equally and predict change in total number (abundance) or density (number per unit area) over time. Simple Model Application Epizootics correlate changes to total numbers of individuals to outbreaks of disease caused by biological agents acting on pollution­weakened populations.
More Complex Population Response Riker Model – relevant to populations with non overlapping generations or to experimental designs with discrete intervals of population increase. It can predict very complex behavior for population dynamics under certain conditions. • • • • Riker Model Components (see equation 10.10) Intrinsic Rate of Increase (r) represents unrestrained exponential growth. Time (t). Finite rate of increase (e r ) applies to, say, annual plants or short­lived insects. Carrying capacity (K) adjusts for the reality that no population continuously exhibits exponential growth.
Importance of Carrying Capacity (K) to Population Dynamics Remain at Increase to K Converge to Oscillate around nota bene Populations also fluctuate wildly with no regard for K
CAUTION FIELD OBSERVATIONS OF POPULATION DENSITIES: OTHER THAN THOSE NEAR K, OR OF THOSE THAT FLUCTUATE WILDLY; DO NOT NECESSARILY REFLECT ADVERSE EFFECTS OF A TOXICANT.
Metapopulations • A relatively new concept within ecotoxicology. • Based on a source­sink hypothesis. • It addresses a complex of populations occupying separate patches in a habitat mosaic (vs. occupying a common space). • It accounts for exchanges of individuals between habitats via migration (vs. assumed lack of mobility).
Metapopulation Models • Are more complex than “population” models (see equation 10.11) • Variables are needed for: – Patch Availability. – Probability of Patch Extinction. – Probability of Reoccupying a Vacated Patch.
Metapopulation Dynamics Vacated (β) (α) Occupied Occupied Occupied Rescue Effect: The probability (β) of Vacated­patch occupation is high because all adjacent patches are occupied. Accordingly, the probability (α) of Vacated­patch extinction is low because of the high proportion of occupied adjacent patches.
Metapopulation Dynamics No Dormant Propagules Tent Caterpillar Eggs Seed Bank Propagule Rain Effect: A density independent phenomenon. Patches with dormant propagules are more likely to be reoccupied and are less prone to extinction than those without.
Metapopulation Dynamics Patch B Migrate Through Patch C Effects Occur Patch A Exposure Occurs Effect­at­a­distance hypothesis: Spatial separation between exposure and effects realization. Recognizes the importance of mobility.
Metapopulation Dynamics Marginal Habitat Marginal Habitat
Marginal Habitat Keystone Habitat Marginal Habitat Marginal Habitat Keystone Habitat: Resources are high in quantity and quality, and occupying individuals are very fit. Just a partial loss of a keystone patch can significantly affect the entire habitat mosaic, and the whole metapopulation.