DALMINE
Overview
Date/time interval
Syllabus
Course Objectives
The course is divided into two modules of 24 hours each. The first aims to develop those chemical and/or biological topics that are at the basis of technological engineering processes for impact mitigation, environmental protection, and natural resource management.
The second module is devoted to methods for statistical modelling of ecological and environmental data and treating uncertainty.
It aims to provide the basic knowledge for treating the most commonly measured variables in ecology and testing scientific hypotheses on the dynamics of quantities such as ecological populations, environmental quality and global warming.
The student will learn to use probability distributions, models for seasonality and long-term trends.
Course Prerequisites
Teaching Methods
Assessment Methods
The final mark will be formulated as a weighted average of the assessments for the two modules.
The final mark takes into account the quality of the skills possessed and the mode of presentation and may be:
(a) Insufficient (below 18/30): The candidate does not demonstrate the minimum knowledge of the topics covered in the course and presents modest expressive skills.
b) Sufficient (from 18 to 20/30): The candidate shows little knowledge of the topics covered, little logical ability, and many gaps in the connections between the topics.
c) Fair (from 21 to 23/30): The candidate demonstrates discrete acquisition of notions, but little depth, sufficient expressive abilities; acceptable mastery of the scientific language; logical abilities and consequentiality in connecting topics of moderate complexity.
d) Good (from 24 to 26/30): The candidate demonstrates a fairly wide acquisition of notions, moderately in-depth, with few gaps; satisfactory expressive abilities and significant mastery of scientific language; good ability to synthesise.
e) Excellent (from 27 to 29/30): The candidate demonstrates a thorough knowledge of the subject; remarkable expressive abilities and a high command of scientific language; good competence and relevant aptitude for logical synthesis; high capacity for synthesis.
f) Excellent (30/30): The candidate demonstrates an extensive and in-depth knowledge of the subject; a high command of the scientific language; a marked aptitude for making connections between different subjects; excellent ability to synthesise. Honours are awarded to candidates who show a complete degree of preparation and appropriate use of the specific vocabulary.
Contents
FIRST MODULE
Introduction to ecology Structure of living systems: from cells to ecosystems. Areas of study of ecology. Stability of environmental systems: resistance and resilience.
Ecosystem ecology
The ecosystem: components and factors. Ecosystem energetics. Solar spectrum. Concept of productivity. Food chains. Grazing and detritus chains. Trophic networks. Energy flow and length of food chains. Ecological pyramids. Lindeman principle. Ecological efficiencies. Ecology of ecosystems: geosphere, hydrosphere, atmosphere. Biogeochemical cycles. Water cycle. Oxygen cycle. Carbon cycle. Greenhouse effect. Nitrogen cycle. Phosphorus cycle. Sulphur cycle and chemosynthesis. Ecological niche. Environmental factors and the limiting factor concept.
Population ecology
Concept of species and populations. Density-dependent growth Population structure. Distribution of organisms in space. Dispersal and migration. Population dynamics. Natality and mortality. Exponential growth of populations. Survival and mortality curves. Life tables. Population growth. Life strategies of organisms. Gradient r-k strategy.
Community ecology
Interactions between populations. Communities. Community dynamics. Interspecific competition. Gause principle and competition models. Predation. Predator strategies and prey strategies. Coevolution. Biodiversity. Diversity indices: index of species richness, dominance, diversity, equi-richness. Community structure and limits. Sörensen index. Factors influencing diversity and causes of diversity loss.
SECOND MODULE
Elements of descriptive statistics:
Data, frequency distributions and graphical representations. Synthetic indices: position indices (mean, median and percentiles) and variability indices (variance and standard deviation). Multivariate data. Correlation and linear regression.
Elements of statistical inference:
Continuous and discrete probability distributions. The normal distribution, the Poisson distribution, outlines of other distributions.
Sampling. The estimation of the parameters of a population. Hypothesis testing and the concept of p-value. Z and t test for the mean. Introduction to inference for the regression model.
Case studies
The lecturer interacting with the class and using the software R will develop the following case studies:
1) comparison between two or more populations;
2) construction and interpretation of an environmental regression model;
3) study of the trend and seasonality of an environmental historical series with correction for autocorrelation.