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Subject Outline

ENVI391 Environmental Science

Faculty of Science, Medicine and Health

School of Earth and Environmental

Sciences

Spring

2013

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Subject Outline Subject code: ENVI391 Subject name: Environmental Science and Systems Credit points: 8 Pre/co-requisites: Enrolment in BEnvSc or BEnvScAdv and completion of

BIOL251, CHEM214, EESC203 and EESC202) Mode of delivery: On Campus Delivery location: Wollongong

Version history

4th edition Dr Tim Cohen /Earth & Environmental Science 2013

3rd edition Dr Tim Cohen /Earth & Environmental Science 2012

2nd edition Dr Tim Cohen/Professor John Morrison/Earth & Environmental Science 2011

1st edition Professor John Morrison/Earth & Environmental Science/Assoc. Professor Ron West/Biological Sciences / Faculty of Science

2010

© University of Wollongong 2013. All rights reserved.

Copyright and Disclaimer No part of this work may be reproduced without the prior written consent of the University of Wollongong. All requests and enquiries should be directed to the Vice-Principal (Administration), University of Wollongong, Northfields Avenue, Wollongong NSW 2522 Australia. Within Australia telephone (02) 4221 3920; international +61 2 4221 3920. http://www.uow.edu.au/about/disclaimer/index.html The University of Wollongong attempts to ensure that the information contained here is correct at the time of production, however, sections may be amended without notice by the University in response to changing circumstances or for any other reason.

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Contacts Subject Co-ordinator Name: Dr Tim Cohen Faculty of Science, Medicine and Health

Location 41.152

Telephone 61 2 4221 3492 Email smah_student_enquiries@uow.edu.au

Consultation mode and times: _________________________________________________________________________

Lecturers Office Phone Email Consultation times

Tim Cohen 41, G32 4239 2375 tcohen@uow.edu.au Tues 10.30 – 12.30

Sam Marx 41, G31 4221 5318 smarx@uow.edu.au By arrangement

Ben Gooden 35, G15 4221 3436 bengooden@gmail.com By arrangement

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Subject Information Outline This subject provides an interdisciplinary approach to environmental science, building on the knowledge gained through the first and second year BEnvSc (Hons) and BSc (Environment) programs. An investigation into the degradation and rehabilitation of both rural and urban terrestrial environments will be undertaken, primarily in terms of the interactions between physical, biological, chemical and geological factors and processes. The course is structured around two main themes:

1. Environmental Planning & Impact Assessment 2. Ecosystem Science and Applied Environmental Management

The first theme provides the regulatory context to environmental science (2-3 weeks) whilst the second (10 weeks) focusses on specific and current global issues, including climate change, population growth, water and natural resource management, rural and urban degradation and rehabilitation. The ecosystems to be covered will include a selection from coasts, estuaries, wetlands, rivers, arid and semi-arid rangelands. Existing and potential impacts on these ecosystems, as well as the social and political environment which influences their management will be addressed. Learning Outcomes The aims of this subject are to introduce students to the multidisciplinary nature of complex environmental science issues and problems, to prepare students for project-based environmental science research and to prepare students for working in an environmental science profession. Through successful completion of this subject students will be able to:

1. describe the inter-relationships between the biophysical aspects of many of the Earth's major systems, including terrestrial and marine (coastal and estuarine) environments;

2. explain important environmental management concepts, such as coastal zone management, total catchment management, natural resource management and climate science;

3. demonstrate competence in collecting, analysing and interpreting field and laboratory data and scientific results, through the preparation of reports and oral presentations;

4. acquire environmental information for research and report purposes from a variety of formats (written, electronic, laboratory and field experiments); and

5. demonstrate work related skills such as: literature searches, understanding OH&S issues, interview skills and resume and research proposal preparation.

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Faculty Graduate Qualities Valuable qualities gained by UOW graduates are essential for gaining employment and making an important contribution to society and their chosen field – further information is available at http://www.uow.edu.au/about/teaching/qualities/

Engagement in this subject will contribute to each student’s development of the following UOW Graduate Qualities:

Informed

• Comprehensive knowledge of an area of Science and well-developed skills in using relevant technologies

• Awareness of the international context in which advances in Science are made and applied

Independent learners

• Critical thinking skills • Scientific approach to the acquisition, analysis, and interpretation of data

Independence in seeking to extend knowledge through ongoing research, enquiry and reflection

• Problem solvers • Application of creative, logical and critical thinking to scientific problems

Effective communicators

• Well-developed written, oral & aural communication • Effective collaboration and teamwork across a range of settings and cultures

Responsible

• Ethical decision making • Respect for diverse opinions, professions, and cultures

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Lecture/Tutorial/Laboratory Times Refer to http://www.uow.edu.au/student/timetables/index.html for an up-to-date timetable. This subject is composed of 2 hours of lectures, 8.30 - 10.30 am (Building 41,204), and 3 hours of practical each week. 4.30 – 7.30 pm (Weeks 2 and 4, Building 17 Lab 110; all other weeks will be in Building 41-157,). The subject includes a COMPULSORY 3-day field trip which accounts for 24 hours of the practical classes. This field trip starts on Thursday 29th August at 7pm at Riversdale (property owned and managed by Bundanon Arts Trust) on the Shoalhaven River and concludes on Sunday 1st September at the end of the day. This field trip will cost between $50 and $100.

Study Time Students should note that UOW policy equates 1 credit point with 2 hours of study per week that includes lectures and tutorials. For example, in a 6 credit point subject, a total of 12 hours of study per week is expected.

ENVI491 and ENVI391 are both 8 cp subjects. Each credit point for a subject has a value of 2 hours per week. There are 5 formal contact hours so students should be spending approximately 10 - 11 hours in addition. These additional hours should be spent completing written assignments, preparing for practicals and/or reading background material provided.

Recommended Readings A number of readings will be made available on the ENVI391/491 Moodle website each week. In addition the following texts are available from library and are useful but not definitive

Cunningham, William P. & Mary Ann Cunningham. 2005. Principles of Environmental Science: Inquiry & Applications (3rd Edition), McGraw-Hill.

O'Riordan, T. 1995. Environmental Science for Environmental Management. Longmans.

Aplin, G. 1998. Australians and their Environment. An Introduction to Environmental Studies. Oxford University Press.

Australian Academy of Science. 1994. Environmental Science. Australian Academy of Science.

The recommended readings are not intended as an exhaustive list and students should use the Library catalogue and databases to locate additional resources.> e-Learning This subject has materials and activities available via eLearning (Moodle). To access eLearning you must have a UOW user account name and password, and be enrolled in the subject. eLearning is accessed via SOLS (student online services). Moodle - http://uowblogs.com/moodlelab/files/2013/05/Moodle_StudentGuide-1petpo7.pdf

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Lecture Schedule: ENVI391/491 – Tuesday 8.30 – 10.30 am, Building 41, 204

Wk Date Topic

Environmental Planning & Impact Assessment

1 30th July Background to Environmental Science (TC) Planning and legislation in environmental management (TC)

2 6th August Conservation vs Rehabilitation: the public and private debate (TC)

3 13th August Landslides and their management (Guest lecture)

Ecosystem Science and Applied Environmental Management

4 20th August Natural Hazards (SM)

5 27th August Coastal and estuary zone science and management (TC)

Shoalhaven field trip Thursday 29th August – Sunday 1st September

6 3rd September Rural landscape degradation and remediation (TC)

7 10th September Erosion control – hillslopes to rivers (Guest lecture)

8 17th September Urban bushland management and remediation (TC)

9 24th September Assessment and prioritisation in catchment management (TC)

Mid-session break 30th September – 4th October

10 8th October River and water management in Australia (TC)

11 15th October The role of geoarchaeology in environmental decision making (guest lecturer)

12 22nd October Climate Science and management (SM)

13 29th October Course review

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Practical Schedule: ENVI391/491 – Monday 4.30 – 7.30 pm, Building 41, 157

Wk Date Topic

Environmental Planning & Impact Assessment

1 29th July No Practical

2 5th August Remembering what statistics are useful for? (BG)

3 12th August No Practical

4 19th August Using statistics to look at geo and bio data (BG)

5 26th August LIBRARY SESSION: Research Databases & field trip planning (TC)

Shoalhaven field trip Thursday 29th August – Sunday 1st September

6 2nd September Post-field trip data compilation

7 9th September Group based data analysis

8 16th September No Practical

9 23rd September Project presentations

Mid -session break 30th September – 4th October

10 7th October Public Holiday – no practical

11 14th October Local river management field trip

12 21st October Individual research presentations/project submission

13 28th October Individual research presentations

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Assessment ASSESSMENT 1 – Individual project proposal – 10%

A succinct outline (maximum 1500 words) of your proposed research topic including: aims, scope, research methods, expected outcomes from proposed research, and incorporating a summary overview of the relevant literature. Title page, table of contents, short abstract (½ - 1 page), tables, figures (or photos), and references are not included in the word count. Students are encouraged to use dot points and tables, where appropriate.

Due on Wednesday 14th August – 2.30 pm – Week 3

ASSESSMENT 2 - Group research project – 25% (20+5)

Design, carry out and report on a sampling program which addresses a major environmental or resource management issue associated with one of the selected locations within the Shoalhaven catchment. Each group will be given one aspect to investigate.

Reporting on the assignment will be in the form of:

An oral presentation (5%). Oral presentations will be made from every team member, outlining some part of the project during the tutorial/practical session in WEEK 9, Monday 23rd September. Each group oral submission, which will be 20 – 30 minutes in duration, MUST be accompanied with a print out of the presentation with accompanying notes and references with each slide marked as to who in the group is presenting each component of the talk.

A team report (20%). This report should be compiled by the team as a whole and submitted as a major report for the Subject.

Due on Wednesday 9th October - WEEK 10.

ASSESSMENT 3 – Individual research project – 25% (15 +10)

This assessment allows you to choose a topic from the list provided or from a topic of your choice (to be made in consultation with the subject co-ordinator). The major component of this assessment (15%) can be in a format of your choice. It can be as a poster, a multi-media presentation, a short documentary or a standard scientific report. A more comprehensive assessment outline will be provided in Week 1.

Oral presentation (10%). An oral presentation will be made by individuals for their own research project. This will be 10 minutes in duration and will be accompanied with a print out of the presentation with accompanying notes and references.

Due on Monday 21st October - WEEK 12.

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Minimum attendance requirements All students must attend lectures and designated practicals and submit all assessment tasks to minimum competence (see below). The field trip is a compulsory component of the trip and must be attended. Minimum performance requirements Students need to complete and submit each assessment task. A minimum standard (25%) must be achieved in all assessments in order to obtain a passing grade, except in the case of the final exam where a Faculty requirement states students must obtain a mark of at least 45% for a 300- or 400- level subject in order to pass the subject.

Students need to complete each component at the level specified.

Component Minumum Standard Final Examination 300 level – 45%

400 level – 45%>

All other assessments >25% Students who do not meet the minimum performance requirements as set out in the Subject Outline may be given a Fail grade or TF (Technical Fail) grade on their Academic Transcript. See the General Course Rules at http://www.uow.edu.au/handbook/generalcourserules/index.html Summary

Task Title Weighting Due Date Assessment 1 Individual Research Project

Proposal 10% Wed 14th August

Assessment 2 Group Project 25% (20+5) Oral – Wk 9 Mon 23rd Sep Report – Wk10 Wed 9th October

Assessment 3 Individual research project 25% (15+10) Oral & report – Wk 12 Mon 21st Oct

Examination Final exam 40% Examination Period

Performance grades

HD High Distinction 85–100%

D Distinction 75–84%

C Credit 65–74%

P Pass 50–64%

PS Pass Supplementary 50%

F Fail (unsatisfactory completion) 0–49%

TF Technical Fail No mark recorded

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Scaling Marks awarded for any assessment task (including examinations) may be subject to scaling at the end of the session by the Unit Assessment Committee and/or the Faculty Assessment Committee (FAC). Marks will only be scaled to ensure fairness/parity of marking across groups of students. Scaling will not affect any individual student’s rank order within their cohort. For more information refer to Assessment Guidelines – Scaling http://www.uow.edu.au/about/policy/UOW058609.html Submission and Return of Assessment Items Assessments are to be submitted in two (2) formats.

1. ONLINE SUBMISSION: You must submit an online electronic version (either a pdf or word document) via the ENVI391/491 Moodle site (through Turnitin). You will be able to review your similarity report prior to submitting the final version of your document. To review a similarity report prior to final submission you must allow 24 hours before you can submit another version.

2. HARDCOPY SUBMISSION: A hardcopy version must be submitted at SMAH CENTRAL by 2.30 pm on the due date. Assignments submitted through SMAH Central must have a SATS Assignment Coversheet attached to the assignment. Instructions for generating a coversheet can be found on the SMAH Central web page. Assignments submitted through SMAH Central must be submitted by 2:30 pm on the final day for submission to guarantee being recorded as submitted on time.

The SATS Assignment System date stamps when an assignment is recorded as being submitted, but does not time stamp. Having a 2:30 pm final submission time ensures that assignments submitted on the due date are recorded in SATS by 5:00 pm when SMAH Central closes.

Assessments can be collected from SMAH central and students will be notified by email when assessments are ready for collection. Uncollected assessment tasks will be destroyed 21 days after the release of marks for that session. Students are advised to keep an electronic or hard copy of all submitted assessment tasks except in circumstances where this is not possible e.g. where the task is submitted at the end of activity in which it was completed. Submitting an assignment at SMAH Central Assignments submitted at SMAH Central MUST have a SATS (Student Assignment Tracking System) coversheet attached to the front of the assignment. Instructions for generating a coversheet can be found on the SMAH Central web page.

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For an assignment to be successfully submitted at SMAH Central please note the following:

• The coversheet must be signed and dated • The assignment must have the correct coversheet i.e. the correct subject code and

tutorial group (if applicable) • A legible barcode with all numbers and digits below e.g. UOW20121007656 • Assignments must be submitted by 2.30pm on the due date

If an assignment is submitted to SMAH Central without any of the above we will contact you and advise that you need to return to SMAH Central with the correct coversheet. Your assignment won’t be recorded as being submitted until the correct coversheet is attached. This might mean that the assignment is recorded as being submitted late. Late Submission All assessment tasks are to be submitted on the due dates as specified in this Subject Outline. Assessment tasks submitted late will be penalised by the deduction of 10% of the maximum possible mark for the assessment task per calendar day or part thereof. Deduction of marks will not result in a negative mark.

Note that assessment tasks submitted to SMAH Central must be submitted by 2:30 pm on the due date to guarantee being recorded in SATS as being submitted on time.

Extensions will not normally be given but under exceptional circumstances a written request may be made and accompanied by the appropriate documentation (e.g. medical certificate through Student Central). If you are having personal problems that are impacting on your meeting subject deadlines then please see Tim and Student Central ahead of the due dates.

Academic Consideration including Extensions of Time Applications from students for academic consideration should be made only on the grounds of serious or extenuating circumstances. Applications for academic consideration are governed by the University’s Student Academic Consideration Policy at http://www.uow.edu.au/about/policy/UOW058721.html

Do not assume that an application for special consideration will be automatically granted.

Supplementary Assessments Supplementary assessment may be offered to students who receive a mark of 48% or 49%, and are otherwise identified as meriting an offer of a supplementary assessment. The form of supplementary assessment will be determined at the time the offer of a supplementary assessment is made. For more information refer to the Supplementary Assessment Guidelines http://www.uow.edu.au/content/groups/public/@web/@gov/documents/doc/uow112335.pdf.

Note that if you are offered a supplementary examination as the supplementary assessment that you will need to sit the examination in the supplementary examination period.

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Referencing

The Harvard referencing system is used in ENVI391/491 – this is also known as the author-date system due to the order of the information presented. Failure to document adequately and fully is to ignore scholarly rules – and run the risk of plagiarism.

Please consult the UOW library website for further information: http://public01.library.uow.edu.au/refcite/style-guides/html/

Plagiarism

Students are responsible for submitting original work for assessment, without plagiarising or cheating, abiding by the University’s policy on plagiarism as set out in the University Handbook under the University’s Policy Directory. Plagiarism has led to expulsion from the University.

The University’s Academic Integrity and Plagiarism Policy, Faculty Handbooks and subject guides clearly set out the University’s expectation that students submit only their own original work for assessment and avoid plagiarising the work of others or cheating. Re-using any of your own work (either in part or in full) which you have submitted previously for assessment is not permitted without appropriate acknowledgement. Plagiarism can be detected and has led to students being expelled from the University.

The use by students of any website that provides access to essays or other assessment items (sometimes promoted as ‘resources’) is extremely unwise. Students who provide an assessment item (or provide access to an assessment item) to others, either directly or indirectly (for example by uploading an assessment item to a website) are considered by the university to be intentionally or recklessly helping other students to cheat. This is considered academic misconduct and students place themselves at risk of being expelled from the University.

http://www.uow.edu.au/about/policy/UOW058648.html

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Assessment Tasks _________________________________________________________________________ Task 1: Individual Project Proposal Due Date: Wednesday 14th August Weighting: 10% Details: Maximum 1500 word (excluding references and abstract) Criteria for assessment: Using succinct scientific writing with a correct report format,

provide a proposal for an individual research project clearly articulating aims methods and proposed format of project. The proposal must also include a summary overview of the relevant literature. See Assessment 1 outline for full assessment criteria.

Task 2: Group Research Project Due Date: Monday 23rd September – Oral presentation (5%) Wednesday 9th October – Group report submission (20%) Weighting: 25% (20+5) Details: Fully referenced scientific report with figures, tables, table of

content and appendices Criteria for assessment: Using correct report format, providing appropriate data tables

and figures, shows an understanding of the data and its interpretive significance. The literature review should provide a critical analysis and understanding of appropriate references on the topic. See Assessment 2 outline for full assessment criteria.

Task 3: Individual Research Project Due Date: Monday 21st October (project submission and presentation) Weighting: 25% (15+10) Details: Fully referenced scientific report with figures, tables, table of

content and appendices (maximum 3000 words excluding references and abstract) OR poster, multimedia presentation.

Criteria for assessment: This individual assessment piece should demonstrate a sound

knowledge of the issue, present a well referenced and well written literature review along with a good overview of the implications for the management of the issue. See Assessment 3 outline for full assessment criteria

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ENVIRONMENTAL SCIENCE PRACTICAL HANDBOOK

This Handbook presents preliminary information about the practical sessions in ENVI391 & ENVI491. This information may change during the course of the semester.

INTRODUCTION

The Practical Sessions of Environmental Science (ENVI391 & ENVI491) are designed to prepare students to carry out project work concerning environmental issues. In particular, we hope to prepare 491 students for their final year in the BEnvSc (Hons) and 391 for work in environmental organisations and agencies.

Some of the exercises we will be covering may help when students are required to:

design experiments and data collection exercises,

prepare for fieldwork,

analyse data, and,

prepare and present a major team report.

The practical sessions for ENVI391 & ENVI491 are broken down into a number of exercises and a short supervised field trip, carried out during Week 2 to Week 5. This is followed by a much larger project that will be carried out with less supervision and in multidisciplinary teams. This project will involve a field trip to the south coast of NSW at the end of Week 7. During the following weeks, team meetings, additional fieldwork and report writing will be required to follow up this field trip and finalise the report. The schedule of practicals is included in the Subject Outline.

The activities undertaken will include:

Occupational Health and Safety Issues

Sampling Design & Analysis of Variance

Library Session

ANOVA using modeled data

Field Sampling in Lake Illawarra

Team Project

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WEEK 2 and 4:

SAMPLING DESIGN AND ANALYSIS OF VARIANCE

Note: This is meant only as a brief review. Students should refer to texts or to notes from their statistical subjects (STAT252) for reference.

Introduction

Statistical analysis provides researchers with a powerful set of tools to use in determining the impact of activities or in carrying out monitoring programs.

This short description is an introduction to some of the statistics you may use, such as the analysis of variances (ANOVA). However, by this stage of your degree you should have completed an introduction to statistics for the natural sciences, in STAT252. This is meant only as a refresher to prepare you for short projects in this session and next year's major project. No formulas are presented here, again refer back to your notes from STAT252 or refer to a standard statistical text (try the library).

In this brief review we will not consider non-parametric statistics analysis, which, while often not as powerful as parametric statistics, make fewer assumptions about the "homogeneity" or normality of the data sets, and are therefore often used in particular studies or situations.

Populations, Samples and Homogeneity

Some data concerning the environment may provide a high level of accuracy and precision, or even be an exact measure of a characteristic. For example, it may be possible to count all trees within a confined area of ground and make a direct comparison with the exact number of trees in another similar small area. Obviously, such measures are exact and do not generally require the use of statistics. In such cases, the differences are obvious and straightforward.

However, in most cases, we do not have information about the entire population, or the characteristics we are measuring may show variation in time or space (such characteristics are generally termed variables). A population can be defined as the total set of possible values for a variable, whereas a sample is a sub-sample drawn from the total population.

For example, we may need to know the density of trees in a large forested area, or large population of trees. In order to make an estimate of this density, we need to take a number of samples, by counting the number of trees in smaller areas (eg. quadrats of known area). It is then possible to calculate a mean and use it as an estimate of the density of trees in the larger forested area. It is important to realise that the use of mean values, variances and standard deviations, all assume that the data and variances conform to the rules of normality - that is that they fit a normal distribution.

A number of statistical tests are available to test the homogeneity of data sets (such as Cochran's C Statistic and Bartlett's Test for homogeneity of variances). If your data set fails a chosen test of homogeneity, a transformation of the data can be applied. Logarithmic and trigonometric transformations can be used and are justified on the grounds of the nature of the population being sampled.

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For example, trigonometric functions are sometimes used in data involving proportions and log transformations in data where clumping of individuals may be present (eg. schools of fish). (If the data sets still fail these tests, it may be necessary to use non-parametric statistics.)

Sample Design and the Null Hypothesis

To be useful, samples must be representative of the whole population. This makes the sample design of an experiment critical. Usually the samples must be taken randomly from the wider population, and the number of samples (n) taken and size of individual samples should be considered carefully for their adequacy.

Usually, if such measures are being taken, they are being used to make comparisons, for example, between sites or between seasons. A typical question might be, are there more trees within one forested area (or type) compared to another similar area. What is actually tested is the null hypothesis, which is the hypothesis that the two forested areas are not different. If a difference is found, it is then often necessary (depending on the number of groups being tested) to carry out a further test to determine where the difference exists. This involves testing for differences between means in a means comparison test.

When starting a new study, the first question is often: How many quadrats should I take, how big should the quadrats be and how do I randomly sample the area in question, in order to give me reliable estimates? Either the existing literature or preliminary experiments can be used as a guide.

Measures of Spread and Reliability

The mean is a measure of the central tendency of the data, but it is also important to understand how much variation or spread there is in the samples. One of the most commonly used measures of spread is the variance. Also used widely is the standard deviation which is the square root of the variance. Standard error relates directly to a statistic, such as a mean, variance, etc. and is a measure of reliability of the statistic.

The standard error is often quoted or graphed when reporting the mean results of sampling programs. (eg. The mean density (±SE) of trees within the forested site was 15 (±5) trees per hectare)

A confidence interval can also be calculated for a mean. To calculate a confidence interval we need to have a measure of the spread (eg. standard deviation), the number of samples taken and nominate a level of confidence we wish to have in the data. For example, a 95% confidence interval can be calculated. Provided the samples were taken in such a way as to be representative of the population as a whole, we should have 95% confidence that the true population mean is within the upper and lower bounds of this confidence interval.

Significance Testing and ANOVA

As environmental scientists, we often need to know whether the estimates of the population mean of one or more samples are different from the mean of another set of samples.

The steps involved in testing for such differences may be:

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• to carefully formulate a null hypothesis, • to choose a level of significance (probability p), usually 0.01 or 0.05 • to design an appropriate experiment and carry out a sampling program • to test for homogeneity of the variances for the data sets • to estimate the means • to calculate the probability that the observed differences between means is due to

chance or is due to a significant difference between the treatments • to accept or reject the null hypothesis • to test for differences between individual means.

The Student's t test is a useful procedure for testing for differences between two small samples (usually n<30). This test uses the ratio of the difference between the two means and the standard error of this difference to calculate the t-statistic. A table of the Students t-distribution at given probabilities and for a known number of degrees of freedom (df, n-1) is used to test the null hypothesis. Obviously the calculated value of t will be larger if the difference between means is large and the standard error is small.

Analysis of variance (ANOVA) is one of the main methods used to test a null hypothesis for larger samples and where more than one factor or treatment is being considered. In an analysis of variance a null hypothesis is transformed into a model and this model is tested for goodness of fit. In carrying out an ANOVA we attempt to partition the variability of the sampled data into two parts, the within treatment variance and the between treatment variance. This is essentially the variance of the individual samples from the mean of the group and the variance of the individual samples from the overall (or grand) mean. The Sum of Squares (SS) and degrees of freedom (df) are used to calculate the mean squares, which are the estimates of variances used in the analysis. Factors or treatments are terms applied to the groups being studied.

ANOVA also tests the null hypothesis. There are two possible outcomes and two types of errors that have been identified. A Type I Error is when the null hypothesis is rejected, when it is true. The chance of a Type I error should be equal to the significance level that has been set (eg. at p=0.05, there may be slightly less than 5 Type I Errors for every 100 experiments).

A Type II Error is when the null hypothesis is accepted when it is false. The probability (ß) of this occurring cannot be as easily determined as a Type I error and is referred to as the power (1-ß) of the statistical test. Generally, the power of an ANOVA is considered adequate if it is above about 0.80. In other words, there is a 20% chance of making this type of error. It is important to realise that if you take too few samples, it is unlikely a significant difference will be found between the factors in an experiment - the power of the experiment is too low. Power is affected by sample size, the spread of the data (variances), the actual differences between the population means (called the effect size) and the significance level at which the test was carried out.

If a significant difference is found, and there are more than 2 means, it remains to test which of these means are significantly different from the others. This is achieved by a means comparison test, such as the Tukey Kramer HSD (Honestly Significant Differences) test.

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Week 2 - Practical aim:

The aim of this practical is to provide a brief yet informative overview of the basic sampling designs used in the physical and natural sciences, and of the methods used to analyse data. This practical has been structured to investigate the following questions:

How do we design a robust and representative sampling regime?

What is the hypothetico-deductive scientific model and how are null hypotheses designed and tested?

What are parametric statistical analyses (i.e. regression, ANOVA)? What are their limitations and assumptions?

What are the effects of sample size on estimates of the mean and variance? How do we determine the best level of sampling intensity?

What are the effects of sample size on the homogeneity of variance, normality of the data and significance of the tests?

What is the best way to graphically represent and interpret analysed data?

SECTION 1: Regression analysis

The very basic aim of parametric statistical analyses is to determine how well one variable (the independent one) predicts another (the dependent variable). We’re all familiar with this process: remember y = mx + b from school, where “y” is dependent on changes in “x”, which in turn is influenced by the slope or gradient “m”? In statistical analyses we must account for extra error within the model, which means the equation we use is y = mx + b + error, where error is all the fluffy variation within the data that we cannot account for.

Regression analysis is used to determine the response of a dependent variable to an independent or predictor variable, and is commonly used in environmental studies. The most common type of regression is a linear model; that is, a straight-line relationship between two variables. Think of the following questions: is tree growth related to soil pH; does fire frequency affect soil carbon concentration; is water velocity related to river depth?... all of these can be answered by doing a regression analysis.

The analysis tests the null hypothesis that the gradient of the line-of-best-fit that describes the relationship between the two variables is not significantly different from to zero (i.e. that the line is flat), and that there is no relationship between the two variables.

The regression analysis is able to:

Describe the relationship between the two variables,

Determine the proportion of variation of the dependent variable that is explained or predicted by the independent variable,

Produce a model that can be used to predict new response values.

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How do you interpret the statistical outputs from a regression analysis? There are three key parameters which must be inspected to interpret a regression analysis: R2, F and P.

R2 = The proportion of variation in “y” that can be predicted by “x”. This is a relative measure of how well the independent variable explains the variation in the dependent variable. It ranges from 0-1, but can be expressed as a percentage by multiplying by 100. For example, If R2 = 0.89, then 89% of the variation in “y” can be explained by “x”, which is a very good fit.

F = In its purest form, the F-value is calculated as the “explained variance/unexplained variance”. The greater the ability of the x-values to explain the variation in the y-values, the explained variation will be high, the unexplained variation will be relatively low, and the F-value will increase in value.

P = The P-value is the most important of the lot; it describes how confident we are that the null hypothesis is true. In other words it reflects how much evidence there is that the null hypothesis can be rejected and, thus, that the relationship between “y” and “x” is real. The P-value is usually set at a maximum of 0.05. When P<0.05, we can be more than 95% sure that the null hypothesis can be rejected; that is, there is a significant relationship between the dependent and independent variables. When P>0.05, it is unreasonable to reject the null hypothesis, and it must be concluded that there is no significant relationship between the two variables.

*The key to interpreting the output parameters of a regression analysis is to first inspect the P-value to see if it’s less or greater than 0.05, then to inspect the R2-value to see how much of the variation is explained by the independent variable.

E.g. A researcher was interested in determining whether the number of shrubs in a plant community influences the size of resident lizards. The following is an output from JMP showing this relationship:

Linear Fit

Lizard length = 1.3057437 + 1.0289717*Number of shrubs

0

5

10

15

20

25

30

35

40

45

Liza

rd le

ngth

0 5 10 15 20 25 30 35Number of shrubs

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Summary of Fit

RSquare 0.705836

RSquare Adj 0.690354

Root Mean Square Error 6.165051

Mean of Response 12.77143

Observations (or Sum Wgts) 21

Analysis of Variance

Source DF Sum of Squares

Mean Square F Ratio

Model 1 1732.7737 1732.77 45.5899

Error 19 722.1492 38.01 Prob > F

C. Total 20 2454.9229 <.0001

Parameter Estimates

Term Estimate Std Error t Ratio Prob>|t|

Intercept 1.3057437 2.166443 0.60 0.5538

Number of shrubs 1.0289717 0.152394 6.75 <.0001

In this example, the null hypothesis was rejected; that is, it was accepted that lizard length was significantly related to shrub density within the community (R2 = 0.71, F1,19 = 45.59, P < 0.0001).

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Now it is your turn. Read the scenario and answer the following questions:

Scenario: The expansion of urbanization along the coast is placing pressure on native vegetation and resident bird species. To identify priorities for bird conservation, land managers need to know which habitat features are having the greatest influence on bird diversity. In the following study, a researcher surveyed the number of bird species occupying coastal forest patches across a gradient of patch sizes with different adjacent urban housing densities. In total, 38 patches of coastal forest were surveyed for the number of bird species for 60 minutes using circular plots of 50 m radius. Within each plot, the percentage foliage cover of native canopy trees was also measured. It was predicted that canopy cover and patch size would have a positive influence on the number of bird species, and that the density of adjacent houses would have a negative effect.

Question 1) What are the null hypotheses being tested (hint: there are three).

1.

2.

3.

How do you run a regression analysis? The statistical package we will use for this is called JMP. The method is simple:

First, copy the data from the excel document labelled “Bird data-ENVI” and copy it into the first cell of a new JMP worksheet (Provided in the ITS Lab)

Click “Analyze” and select ‘Fit Y by X”.

You will see your list of variables on the left, and a box labelled “cast selected columns into roles” – these will allow you to build a model to test your null hypothesis.

Select “number of bird species” and click “Y response” to add it to the box for dependent variables; select “forest patch size” and click “x factor” to specify that it is the independent variable. Click “OK”.

At this point you will see a dot plot showing the distribution of bird diversity (y-axis) versus forest patch size (x-axis). To run the regression you need to click on the red arrow in the top left-hand corner of the graph and select “fit line” – this will run the regression analysis.

Now run another two regression analyses, one each for canopy cover and urbanisation density versus number of bird species.

Question 2) What are the results of the regression analyses for each test? Do you accept or reject each of the null hypotheses? What are the R2-values for each test? What do these represent? Fill in the table below to summarise your data.

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Predictor F-value P-value R2 Significance

Canopy cover

Urbanisation

Patch size

Question 3) Are all the habitat variables important predictors of bird diversity? Which is the most important?

Question 4) What are the conclusions we can make about the conservation of birds in urban landscapes?

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SECTION 2: ANOVA

Analysis of variance is another form of a general linear model where the y-variable is modelled against the x-variable to detect a relationship between them. The model is identical to that for a regression analysis; however, the independent variable is categorical, not continuous (although the dependent variable “y” is continuous in both cases).

In an ANOVA, the independent categorical variables are called “factors”, and each factor is comprised of a number of “treatments”, one of which is the “control” against which the treatment effects are compared. The ANOVA enables us to compare mean values among different treatments within a factor to determine whether the treatment effects are real and significant.

*The way that the data are analysed and interpreted in JMP is identical to that done for a regression analysis, although the R2 is not necessarily reported.

Read the scenario and answer the following questions:

Scenario: A study was done to assess the effects of weed invasion and weed removal on nutrient dynamics in coastal sand dunes.

Previous research has shown that exotic weeds (such as bitou bush and lantana) grow faster, live longer and are better at acquiring nutrients (i.e. more competitive) than the native dune plants. It was predicted, therefore, that soil from dunes invaded by weeds would contain fewer nutrients than native soil.

The weed removal protocol comprised mechanical removal of weeds from the roots, poisoning of cut stems and mulching of above-ground weed biomass in situ, followed by revegetation of the dune with native seedlings. It was predicted that the soil from dunes which had had the weed removal treatment would contain higher nutrient levels than native or weed-invaded soil due to the reintegration of weed biomass into the soil.

A number of soil cores were sampled from 45 sand dune locations. Locations were geographically interspersed, selected at random and were allocated to one of three treatments of weed invasion: (1) dunes invaded by weeds; (2) dunes where weeds had been removed and replaced with native plant species; (3) pristine dunes that only contained native plant species and with no known history of weed invasion. Cores were selected at random from each site and bulked to form one soil mass per site. Total nitrogen content (kg ha-1) was then analysed in the lab.

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Experimental design

Question 1) What is the null hypothesis being tested?

Question 2) What is the design of this survey?

What are the independent (also called predictor) and dependent (response) variables?

What are the factors and what are the treatments?

Which is the experimental control (also called reference) treatment?

What is the level of replication (i.e. n-value)?

Are the independent variables continuous or categorical?

Effects of sample size on estimates of the mean and variance?

When undertaking a field survey like this, it is important to determine the level of replication (n) per treatment that will maximise the power of the test (i.e. ensure that the outcomes of the statistical analyses are robust and reliable) whilst at the same time reducing the amount of work we have to do. That is, increasing the efficiency of the survey. In terms of this data this means we need to settle on an n-value that reduces fluctuations in estimates of the mean (average) and the error or variance around the mean. After all, if the error is too variable around the mean, it becomes difficult to detect differences in treatment effects on the response variables.

To determine the optimum n-value, follow these steps in the excel document:

Calculate the mean nitrogen level and standard deviation (SD) for each of the three treatments and identify the one with the highest SD (this represents the treatment comprising the greatest level of error within the data, and the one for which we need to optimise the level of replication).

Within that treatment level, randomly select 1 data point from the available 15, then 2, then 3, then 4, etc. and calculate the mean and standard deviation and so on for these groups until you have selected all 15. Now produce a line graph in excel showing the number of replicates (i.e. n; 1, 2, 3…15) versus the corresponding mean and SD.

Question 3) How do estimates of the variance and mean change with the level of replication?

Question 4) From you graph, what is the optimum level of replication?

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Now perform an ANOVA in JMP using your optimum level of replication.

Export the data from excel into JMP and label each column. There should be one column only for the independent and dependent variables. Right click on each column label, select “Column info…” and inspect the data profile. Ensure that the “data” and “modelling” types are labelled accurately.

Now run ANOVA.

The steps for this are:

Click “Analyze” and select ‘Fit Model”.

You will see your list of variables on the left, and two boxes labelled “Pick role variables” and “Construct model effects” – these allow you to build a model to test your null hypothesis. Note: Because we already ensured that our independent variable (level of weed invasion) is categorical and that our dependent variable (nitrogen level) is continuous, JMP will automatically run an ANOVA for us. If we were to enter both variables as continuous, JMP would automatically run a regression analysis.

Select “nitrogen” and click “Y” to add it to the “Pick role variables” box; select “invasion category” and click “Add” to add it to the “Construct model effects”.

Click “Run model” to perform the ANOVA.

There are two important tables in the analysis output: “Analysis of Variance” and “Effects test”. Read these and identify the important stats parameter: degree of freedom, sum of squares, MSE, F-ratio and P-value. Of these, the P-value is the most important as this tells you whether the null hypothesis is rejected or accepted… i.e. whether there is a difference in the mean nitrogen level among weed treatments.

Question 5) Statistically, do you accept or reject your null hypothesis? Is there a difference in the mean nitrogen level among weed treatments?

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Now ensure that the data are normally distributed and have homogeneous variances.

Assessing normality: ANOVA is generally robust to departures from normal distributions of the data, but it is always a good idea to check that the data is normal before accepting the output of the regression analysis. This can be done easily by inspecting the “Residual by predicted” plot at the bottom of the analysis page in JMP. For the data to be normally distributed, we need the residual data points to be spread evenly below and above the blue dotted line, and roughly in the middle of the x-axis:

Assessing homogeneous variances: This can be done using the Cochrans test, but we will not be doing this today.

Comparing means after ANOVA.

The very last step in the ANOVA is to determine which treatment means are different from each other. To do this we need to run a “post-hoc test” to determine where those significant differences lie.

There are many post-hoc tests that can be used, but the one that JMP uses is called the Tukey HSD test. This is ideal for one-factor ANOVAS, but can also be used for multifactor ANOVAs (although some people do not like to use it for that).

To do this, click the small red arrow in the top left hand corner of the “Leverage plot” (which is located on the top right of the ANOVA output) and click “LSMeans Tukey HSD”. At the very bottom of this output you will see a list of your treatments with a capital letter (either “A”, “B” or “C”) to their right. Treatments with different letters have significantly different mean nitrogen levels.

Differences amongst means can be expressed on a graph by placing these letters above each bar.

Question 6) What are the patterns of variation in mean nitrogen level across the three treatments?...Which means are different from each other?

Question 7) What conclusions can we make from the ANOVA? Do we accept or reject the hypothesis?

-500-400-300-200-100

0100200300400500

TOTA

L N

ITR

OG

ENR

esid

ual

0 200 400 600 800 1000 1200TOTAL NITROGEN Predicted

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Question 8) Copy the ANOVA output into your document and produce a graph in excel.

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SECTION 3. Multifactor ANOVA

Researchers always endeavour to create the simplest statistical model to describe the world… but the world is complicated and multiple habitat variables are likely to interact in complex ways to effect change in some dependent variable.

It is possible to introduce multiple predictor variables within the ANOVA model. The benefit of this is to account for residual error within the model, and create a model that best predicts the variation in the response variable.

For example, most plants are salt intolerant, but may be better able to cope with salt stress when grown in particular soil types. Thus, soil type and salt concentration will interact together to influence plant growth. In another example, birds are important fruit-dispersers for many rainforest trees, but their rates of visitation and fruit removal can be influenced by tree height and the amount of light able to penetrate to the tree canopy.

A multifactor ANOVA has three hypotheses: one for the influence of each independent factor separately on the response variable (thus, two of them), and one for the influence of the interaction between the two predictor variables on the response variable. This third hypothesis is as follows:

The influence of independent variable A on the response variable depends upon or varies with the independent variable B.

Scenario: Freshwater invertebrates are responsive to changes in the health of streams, and are often used to monitor human impacts on the environment. A study was done to assess the effects of different types of streams and cattle grazing on freshwater invertebrates.

Open the excel spreadsheet and inspect the data.

Question 1) What are the hypotheses being investigated (hint: there are three)?

-

-

-

Question 2) What is the design of this survey?

What are the independent (also called predictor) and dependent (response) variables?

What are the factors and what are the treatments?

What is the level of replication (i.e. n-value)?

Are the independent variables continuous or categorical?

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Now perform a two-way crossed ANOVA in JMP (assuming that the level of replication has already been optimised).

Export the data from excel into JMP and label each column. There should be one column for each variable. Again, right click on each column label, select “Column info…” and inspect the data profile to ensure that the “data” and “modelling” types are labelled accurately.

Now run the ANOVA by clicking “Analyze” and select ‘Fit Model”.

Select “number of invertebrates” and click “Y” to add it to the “Pick role variables” box; select “stream velocity” and click “Add” to add it to the “Construct model effects”. Do this step for “Grazing” also. Now we need to add one more factor to account for the interacting effects of these two independent variables on stream invertebrates; that is, the “Cross. To do this click “stream velocity” in the “select columns” box, then click “grazing” in the “construct model effects” box, then click the “Cross” button to add the interaction effect to the model.

Click “Run model” to perform the ANOVA.

How to interpret the results for a multifactor ANOVA

First look at the “Effects test” box. This contains significance estimates (i.e. P-values) for each of the predictor variables. Begin at the bottom of the table. If the interaction term is significant (p<0.05) YOU MUST DISREGARD THE P-VALUES FOR THE TWO SINGLE PREDICTOR VARIABLES ABOVE IT. THESE MUST BE IGNORED BECAUSE THE TEST INDICATES THAT THEY INTERACT COMPLEXLY AND CANNOT BE INSPECTED IN ISOLATION.

If the interaction term is not significant, inspect the two single predictor variables. If these are significant, do a Tukey HSD test to determine where the differences in mean values lie.

Question 5) Statistically, do you accept or reject your null hypotheses?

Question 6) Graph your data in excel to reflect the analyses (Hint: one graph per significant result)

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WEEK 3:

LIBRARY SESSION: RESEARCH DATABASES, REFERENCING & ENDNOTE USE

Introduction

It is essential that all students are capable of undertaking literature searches of the research databases, and can prepare a reference list in a standardised format. The Faculty Librarian will present a brief review session about the library and the use of the various databases, particularly those used in science.

Research Databases

There is a range of databases of literature available through the library web site, and most of those for research purposes in science are listed on the following page:

http://www.library.uow.edu.au/eresources/databases/dbfaculty/dbsci.html

Some of the most used ones are Current Contents, Biological Abstracts and Web of Science. Streamline is an excellent source of Australian materials, including a lot of government reports and “grey” literature (a term which refers to reports not formally published or edited and often of limited availability).

Referencing

The University provides some excellent general materials for students at the following web site, including learning materials useful in thesis and report writing:

http://learning.uow.edu.au/resources/

Of particular interest is the pamphlet on referencing, which can be as a guide for all assignments and reports in this subject:

http://learning.uow.edu.au/resources/LD/referencing.pdf

Note also that for information on how to reference electronic resources you can get a copy of the library’s Citing Electronic Resources from the Library front desk.

EndNote

EndNote is a software package that you can use to manage your references, images, notes and other files. It links your references to a Word document to automatically create a bibliography. This system is widely used in honours report preparation and, in this session, you will be guided in its use.

http://uow.libguides.com/endnote

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WEEK 5:

OH & S, FIELD WORK SAFETY; HUMAN & ANIMAL ETHICS.

Introduction

This is a practical session to discuss issues related to research activities, including:

OH & S

Fieldwork Risk Assessment, and Human & Animal Ethics, and

OH & S and fieldwork safety

Ensuring safe working conditions is everyone’s responsibility and a very serious matter, particularly during field activities. For example, over the last 20-30 years there have been several deaths of students from Australian universities undertaking field work. It is important to understand and manage any risk associated with your laboratory and field work, and not to undertake activities that may have a high risk, or may carry a particular risk to you. OH&S and field safety issues will be discussed in class. There is also quite a deal of information available on-line:

http://staff.uow.edu.au/ohs/

There are a number of forms attached to this Handbook that must be completed prior to undertaking any practical work in this Subject. At the end of this practical session, all students should hand in their fieldwork participation form and their fieldwork risk assessment form for carrying out field work in Week 4.

Human & Animal Ethics

Human ethical issues may be involved in any research that attempts to create information about individuals or groups of people, or people’s ideas property or belongings. Examples are interviews with individuals, community questionnaires or surveys, DNA sampling, property assessments and so on. Any research activities involving human ethics must have the clearance of the Human Ethics Committee at the University. For further information, consult the following web site:

http://www.uow.edu.au/research/rso/ethics/human/

Probably even more relevant to science students is the issue of Animal Ethics, which applies to any experiment of field work associated with vertebrates, such as capture and release of birds or fish, experiments where the endpoint is death, or animal observations. Again approval must be given by the Animal Ethics Committee at the University. For further information, consult the following web site:

http://www.uow.edu.au/research/rso/ethics/animal/