Unit One-Part One - Nature of Science
Nature of science
It is dynamic and testable.
Science as a method for studying nature and designing experiments.
Elaboration: Science is changeable; scientists must access data and prove points.
Characteristics of science:
Studying the natural world by gathering and analyzing data.
Unraveling the mystery of how nature works.
Reliance on data differs from philosophy, ethics, and religion (these groups rely on faith/personal beliefs rather than provable facts).
Scientific Rules (four main rules plus examples)
Rule 1: Patterns can be determined through observation and study; assumes that something needs to be figured out.
Rule 2: Induction: Assume discovered patterns to be true everywhere across time.
Formal idea: for a discovered pattern P, it is assumed to hold for all times t: forall t \, \text{Pattern}(t)
Rule 3: Parsimony: If multiple explanations exist, the simplest is most likely true (Occam’s Razor).
Example: Evolution showing shared traits and common ancestry.
Elaboration: When you lose your key, start with the simplest possible location first; simplest explanations are more likely.
Phylogenetic Tree: Build a tree by sampling traits or DNA sequences; pick the simplest tree (fewest assumptions/branches).
Non-Scientific Example: The vanishing instructor due to a gap in sensory explanation. Senses may not tell how he traveled. Numerous plausible explanations exist (walk, climb, teleport, clone, portal, etc.); the simplest explanation is often correct but not exclusive.
Rule 4: Science uses induction to make generalizations (inferences) based on repeated observations.
Repeated observation example: "All the squirrels I’ve seen have fur." → leads to generalization: squirrels have fur.
Induction generalizes from specific observations to broader claims; however, generalizations can be wrong (e.g., all swans are white was challenged by the black swan, Cygnus atratus).
Historical anecdote: In 1787, England sent convicts to Australia; later, Cygnus atratus (the black swan) was discovered in Australia, challenging the prior generalization.
A model and the role of inference
A model is a complicated explanation of complex ideas and conceptual networks.
Build models using inferences from observations and data.
Induction is necessary but has limits; science does not claim absolutes.
Scientific statements and hypotheses
Must be testable: Propose a hypothesis that is testable and can be evaluated with data.
Evaluate the hypothesis with data.
Measuring the past uses fossils, air trapped in glaciers, etc.; a hypothesis must be phrased in a testable way.
The past can be misconstrued if based on non-measurable evidence; as long as something measurable survives history, testing is possible.
Past vs. Modern comparison examples:
Can measure air from a million years ago through an iceberg.
Viloseraptors: compared to birds (modern birds and dinosaurs relationship).
Studying humans and Neanderthals: a case study
Humans and Neanderthals coexisted; hypothesis initially posited no interbreeding.
About 50,000 years ago, interactions occurred and then dispersed.
Earlier depictions as cavemen modernized; evidence shows Neanderthals wore clothes, created art, lived in social groups, and showed signs of spirituality.
DNA sequencing (stalagmite-based) indicates interbreeding: most modern humans have about 2\% Neanderthal DNA.
The Scientific Method: general flow
A simple experiment follows a flow: Observation → Inference → Model → Hypotheses → Measurements → Data analysis → Evaluate hypotheses.
If a hypothesis is incorrect, revisit the model and test other possibilities.
For big observations, a flowchart-like approach is used: observations lead to inferences, which lead to a model linking inferences to observations.
Hypotheses are derived from the model; measurements are taken; data are collected and analyzed.
If a hypothesis is rejected, consider alternative hypotheses; the process iterates.
Model testing and theory development
If a model withstands repeated testing, it is elevated to a theory.
The scientific community may replace a model with a theory when the theory best explains the data.
Theory in science: a category for the most-supported ideas; no higher level of certainty than the current consensus.
If a theory has a mathematical basis, it may be called a law.
Theory in everyday language may be used to describe an untested hunch.
The terms theory and law are often misused in public discourse; true scientific theories explain facts.
Open-minded skepticism is essential in science, but claims should be based on evidence.
The dynamic nature of science
Dynamic science: science changes with new discoveries and methods; criticism of science often centers on historical assumptions.
Example of evolving nutrition science: fat once labeled bad; later sugar also recognized as harmful.
Case study: Organization of the heavens (Solar System history)
The heavens were studied extensively and used as a calendar;
Planets categorized as Terrestrial (rocky) and Jovian (gas giants).
Early astronomers and models:
Claudius Ptolemy (2nd century A.D., Greek in Egypt): geocentric model with crystal spheres; Earth at center; circular orbits; explained retrograde motion via epicycles; observations were naked-eye.
Egypt as a learning center; instrument development allowed better observations.
Copernicus (1543 A.D., Polish cleric): heliocentric model with the Sun at the center and circular orbits; however, measurement accuracy was limited.
Aristarchus (1st century A.D.) had earlier heliocentric idea; graduate to observational tools allowed refutation of the old model.
Kepler (1609 A.D., German mathematician): sun-centered model with elliptical orbits; improved with Brahe’s data; improved orbital predictions.
The telescope and better data progressively invalidated earlier models; science is self-correcting as new data emerge.
Evolution of major theories: atomic theory of matter; theory of natural selection; plate tectonics.
Ensuring quality science
Data quality is essential; science is data-driven.
Peer review: researchers submit to scientific journals; experts review methods and conclusions; papers may be rejected for poor experimental design.
The role of specialized scientific journals in validating research.
Experimental design and the importance of good design
Sampling bias: biased subject selection can distort results (e.g., selecting only women basketball players or male jockeys).
Uneven pesticide exposure as an example bias.
Independent variable (IV): the manipulated variable controlled by the researcher.
Dependent variable (DV): the measured response.
Controlling variables (CV): all other variables kept constant to isolate the effect of the IV.
Replication and sample size:
More replicates reduce the impact of random variation and help detect true effects.
Confounding variables can obscure cause-effect relationships.
Example experimental design: fern growth under different watering regimes over 6 weeks, with 3 replicates per group:
Group 1: 0 L water
Group 2: 0.5 L water
Group 3: 1 L water
Independent variable: Water
Dependent variable: Biomass after 6 weeks
Rationale for replicates: to avoid odd responses and to identify outliers; more data improve reliability.
Science in Action: Amphibians and wastewater – The Living Filter (Penn State)
Usual wastewater treatment: conventional approach releases treated effluent to surface water; can deplete groundwater and cause eutrophication due to Nitrogen and Phosphorus.
Penn State Living Filter: spray effluent onto soils; soil and organisms remove Nitrogen, Phosphorus, and other impurities; helps replenish groundwater.
“Artificial Rain”: campus wastewater treated and sprayed on-campus; up to 4 million gallons/day; high levels of Nitrogen, Phosphorus, and salts (Sodium, Calcium, Potassium, Magnesium) in effluent; irrigation alters habitat:
Wetland formation: limestone depressions create temporary ponds with regular rainfall augmentation.
More weeds: nutrient-rich irrigation promotes weeds; can cause trees to fall, increasing light gaps.
Ice damage: warm spray leads to ice formation upon release into air; weight can break branches.
Less leaf litter: consistently wet conditions reduce leaf litter, impacting habitat for many organisms.
Increase in pond plant life: irrigation creates swampy ponds with dense duckweed mats.
Eutrophication: excess nutrients feed algae; when algae die, decomposition consumes oxygen, leading to hypoxic/anoxic water and potential fish kills.
Pond eutrophication model (in accompanying packet): winter ice and subsequent spring growth cause changes in nutrient dynamics and oxygen availability.
Amphibians as bioindicators and vertebrate life cycles
Amphibians have complex, multi-stage life cycles; life stages differ in ecology and pollutant exposure.
Amphibians are permeable to water and air; eggs, gills, skin, and organs absorb pollutants easily; they are ectothermic (cold-blooded).
Tadpoles and froglets: rapid growth rates make amphibians useful for experiments;
They occupy a middle position in food webs; their decline signals ecosystem problems.
Vernal ponds and amphibian breeding ecology
Vernal ponds: temporary wetlands that dry out part of the year; frogs breed in late winter/early spring when ponds thaw.
Wood frog (Rana sylvatica): tiny species that breeds in temporary ponds; males arrive earlier and establish territories; territorial dynamics affect mating success.
Jefferson salamander (Ambystoma jeffersonianum): longer than a sharpened pencil; unique to certain regions (Ohio and West Virginia); breeds in separate microhabitats.
Spotted salamander (Ambystoma maculatum): yellow/orange spotted pattern; eggs laid in clumps; eggs differ in arrangement and mass compared to salamander eggs.
Breeding sequence:
First, wood frogs breed in temporary ponds as ponds thaw in late March; males arrive 2–3 days before females; territories established; females assess male calls and select territories; eggs laid and fertilized in water.
Jefferson salamanders arrive after wood frogs; lead to massed egg deposition by spotted salamanders.
Embryos and larvae:
Embryos elongate during development; hatch and become free-swimming larvae.
Wood frog tadpoles are herbivorous but may cannibalize under stress; salamander larvae are carnivorous and predatory.
Impacts of irrigation on vernal ponds:
Chemistry: irrigated ponds show higher nutrient levels and lower dissolved oxygen (DO); DO can drop below 0.5 mg/L, reaching zero within approximately 3 weeks.
Amphibian field experiments and results (pools, hatching, larval survival, terrestrial stages)
Egg mass counts: 10 irrigated ponds vs. 10 natural ponds; spring 1997 and 1998; counts reflect female reproductive output and habitat quality.
Hatching success: 3 irrigated ponds and 3 natural ponds; 5 replicates per species per pond; in situ enclosure experiments to gauge hatch rates.
Larval survival: same replication scheme; daily monitoring for 6 days; cage/enclosure methods to keep eggs/larvae within test sites.
Findings:
Spotted salamanders showed low hatching rates in irrigated ponds due to bacterial blooms.
Larval enclosures in irrigated ponds showed poor survival; field results indicated negative impacts of irrigation on larval stages.
Terrestrial salamander growth in field enclosures showed no clear acute effects; higher body sodium in lab tests suggested stress under wastewater substrates though not necessarily in field conditions.
Overall conclusions:
Ponds irrigation altered pond chemistry and biology; egg hatching and larval survival were reduced.
No acute terrestrial impacts observed in the short term; however, the pond ecosystem and populations were affected overall.
The experimental model supported the hypothesis that wastewater irrigation affects amphibian life stages; monitoring is needed for long-term outcomes.
Tragedy of the Commons (environmental economics and policy)
Definition: a common resource is exploited for individual gain, leading to overuse and depletion.
The Commons: resources not owned by a single entity (examples: rivers, communal grazing lands, atmosphere, international oceans).
Video reference: Econclips on the tragedy of the commons; Lorax discussion as a narrative example of resource depletion.
Historical and modern examples:
Classic pasture example: common grazing land was overexploited as private owners added more cattle to shared pasture.
Modern pollution example: unregulated driving contributes to urban air pollution; smog is a major danger; vehicles contribute to pollution in Atlanta and other cities; regulation varies by country.
Core problem: entities benefit from polluting while society bears the costs; common resources become degraded.
Solutions and policy levers:
Property rights and assigned ownership of scarce resources can reduce overuse.
Bottom-up institutions and governance mechanisms to coordinate use and limit exploitation.
Market-based and regulatory approaches to internalize the external costs of pollution.
Economic considerations:
Profit equation: \text{Profit} = \text{Revenue} - \text{Costs}
In competitive markets, raising revenue is difficult; reducing costs (e.g., layoffs) is a common but ethically problematic route.
Top-down regulation can correct externalities but implementation varies by jurisdiction.
Human behavioral factors:
Problem 1: Humans are intelligent but sometimes selfish, prioritizing personal gain over long-term sustainability.
Problem 2: Short-term planning tends to dominate over long-term consequences.
Problem 3: Perceived exceptionality leads to risk-taking with potential harm to others.
Lesson: The tragedy of the commons arises from a mix of biology, economics, and political structure; addressing it requires coordinated policy, incentives, and cultural change.
Narrative linkage: Lorax and other stories illustrate how unchecked resource extraction leads to ecological collapse and social consequences.
Real-world relevance and connections
The dynamic and testable nature of science underpins everyday decision-making (policy, health, environment).
Experimental design informs how we test hypotheses about ecological and social systems.
Bioindicators like amphibians help monitor environmental health and the effectiveness of wastewater practices.
Understanding the tragedy of the commons informs policies on pollution control, resource allocation, and sustainable development.
Key equations and quantitative references (LaTeX)
Induction generalization across time: forall t \, \text{Pattern}(t)
Profit in environmental economics: \text{Profit} = \text{Revenue} - \text{Costs}
Notation for scientific reasoning and time-based analysis is used where appropriate in models and tests (no single universal equation governs all cases, but these representations illustrate core ideas).
Quick recap of numerical anchors from the transcript
Neanderthal DNA in modern humans: \approx 2\%
Year of Copernicus’s publication: 1543 A.D.
Kepler’s astronomical work: 1609 A.D.
Timeframe for vernal pond breeding cycles: late winter to early spring (thawing in late March noted).
Observational and experimental replication counts in amphibian studies: multiple ponds (e.g., 10 irrigated vs 10 natural); 5 replicates per species per pond; 3 replicates for some hatching tests; 6-day larval monitoring.
Connections to foundational principles
Emphasizes the empirical basis of science: observation, measurement, and data-driven inference.
Highlights the iterative, self-correcting nature of science as new tools and data become available.
Stresses ethical implications of research design, wildlife impact, environmental stewardship, and policy decisions.
Bottom-line takeaways
Science advances by building models, testing predictions, and refining or replacing theories with accumulating evidence.
Experimental design quality (IV, DV, CV, replication) determines the reliability of conclusions.
Real-world case studies (Living Filter, amphibian ecology, tragedy of the commons) illustrate how scientific reasoning translates into environmental management and policy challenges.
End-of-notes quick questions for review
What distinguishes induction from observation in scientific reasoning? Why is induction powerful but limited?
How does a model become a theory, and what distinguishes a theory from a law?
How can experimental design mitigate sampling bias and confounding variables?
In the Living Filter case, what ecological processes lead from nutrient input to potential hypoxia in ponds?
What policy instruments can address the tragedy of the commons in modern contexts?