Science and natural resources management

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Science and natural resources management

• Dr. David Valentine
• NRM 304 Perspectives in Natural Resources
  Management
• University of Alaska

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Negative human population impacts?

• Increasing demands on food production
• Increasing conflict for resources
• Discernible impact on global climate
• Increasing species extinctions through land-use
  change
• Increasing vectors for disease

3
Positive human population impacts?

• Decreasing resource prices
• Global warming may be good for life
• Increasing quality of life (per capita GDP)
• Increasing availability of health care

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Foundations

• Values/ethics what we want
• who is we?
• Public policy constraint on activity
• includes precedent
• Science analysis of non-economic consequences
• Economics analysis of resource flows and
  allocation

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Policy flow chart
Rational
Irrational
Values Ethics
Actual consequences
Fore/hindcast consequences
Public Policy
Random factors
Economics
Activity
Science
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What is Science?

• A parsimonious way of knowing
• Knowledge via the scientific method
• Legal definitions
• Pre-1994 What scientists do
• Post-1994 Establishment of fact
• Headlights

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Traditional knowledge

• Is valuable as experience-based wisdom and guide
  for decisions
• especially when no or few other data exist
• science in policy flow chart if it
• is based on factual reliable information
• makes use of repeatable measurements
• is open to testing against data
• changes in response to more or better information

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Basic and Applied science

• knowledge for its own sake
• funding difficult to justify
• quality of success is unpredictable

• solve a specific problem
• funding easy to justify
• quantity of success is unpredictable

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The scientific method

• Make observations, ID question
• Generate hypothesis and predictions
• Design experiment
• Collect and analyze relevant data
• Interpret data and test hypothesis predictions
• Conclude Reject--or fail to-- hypothesis,
  extrapolate
• Never prove, only fail to disprove

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Scientific method example, part 1

• Observations
• Human population is increasing
• People consume resources
• Wealthy people consume more resources
• Technology modifies impact of consumption
• Hypotheses
• H0 Impact ? f(population)
• H1 Impact population ? affluence ? technology
• H2 Impact some other f(population)

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Scientific method example, part 2

• Specific prediction derived from hypotheses
• Some or all measures of impact (resource
  degradation or price, species extinctions,
  pollution, disease, war, etc.) will increase with
  human population within some range
• Ideal experiment
• Vary human population, affluence, and
  technological advancement in factorial design
  across multiple habitable planets
• Monitor variables of interest

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Scientific method example, part 3

• Interpretation Compare data with predictions
• Conclusion reject 2 of 3 hypotheses
• Limitations
• If H0 not rejected, did sample size give
  sufficient power?
• Range of independent variables finite
• Range of dependent variables finite
• Acceptability never addressed
• Others possible

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Science back to reality

• Ideal answer not attainable for human population
  growth impacts
• constrained and confounded independent variables
• will be after the fact
• too late for action
• well still argue over cause effect
• N 1
• Must base policy on imperfect predictions of
  consequences!

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Logical reasoning

• Inductive generate broad theory from specific
  observations
• Used in generating hypotheses
• e.g., IPAT
• Deductive extract specific predictions from
  broad theory
• Used in generating testable predictions from
  hypotheses
• e.g., species extinction rates will increase in
  proportion to human population increase

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Sources of error in science

• Specification
• selection of problem
• experimental design
• Sampling
• Measurement

• Error usually avoidable
• problem is appropriate
• experiment tackles problem
• More better
• Unavoidable error

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Religion and Science

• Seeks truth
• Explains nature
• Employs logic
• Deductive gt inductive
• Rich history of scholarship
• Arbiter of truth is consistency with doctrine
• Shoulds prevalent

• Seeks truth
• Explains nature
• Employs logic
• Inductive gt deductive
• Rich history of scholarship
• Open to and demands testing against objective
  measurements
• Shoulds absent

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Science and Technology

• Science seeks knowledge, while technology applies
  it
• Both use the other
• science to make measurements
• technology to refine or design tools
• Logic
• Science emphasizes inductive
• Technology emphasizes deductive

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Science in the Ideal World

• Basic applied research funded
• Problems addressable
• Experimental designs address problems correctly
• Consequences of activities non-ambiguous
• Replication is adequate for generalization
• Scientists are perfect
• never make errors
• never say should

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Science in the Real World

• Science costs , but mistakes cost
• Correlation vs. causation
• Natural vs. controlled experiments
• In natural resources
• Case studies are the rule
• Generalizations are difficult
• Unknown gtgt known
• Lame predictions without specific data
• Scientists are people

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Science and Public policy challenges

• Some policymakers are not well-versed in relevant
  science, but think they are
• Limitations of questions science can answer
• within available budget
• within context of previous work
• Uncertainties are inevitable
• Nothing is ever proven
• Errors in sampling and measurement unavoidable

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Science and Public policy downside

• Contrasting cultures
• Mutual suspicion, disrespect
• Poor communication
• Blurred responsibilities as experts
• Consequences
• Demands unclear or rejected
• Poor incorporation of knowledge
• Relationship of science and public policy
  confused, even by lawmakers

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Take-home messages for resource managers

• Being explicitly based on data, scientific
  knowledge is most reliable basis for predictions
• Science provides improving information about
  consequences of events or activities
• Science is not balanced against other
  foundations any more than a cars headlights are
  balanced against its engine

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Human populations Malthus H1

• Thomas Malthus predicted starvation based on
  observations of
• geometric increase in human population
• arithmetic increase in food production
• Was Malthus wrong? Two answers
• Yes, because global food production rates have
  kept pace with global human needs
• Too soon to tell (when would this not be true?)

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Science human population

• Fruitful inputs
• project human population over time
• assess climate change and other risks of impacts
• provide knowledge needed for improving technology
  for food production, impact mitigation, etc.

• Less fruitful inputs
• calculate optimum human population
• prove link to climate change and other likely
  impacts
• project pace and direction of technological
  advances