ENV11115 Case Studies in Applied Ecology Assessment Brief | ENU

Upon successful completion of this module you will be able to:

LO1: Evaluate ways in which wildlife management problems are identified and framed, and how this relates to generating study aims and testable hypotheses.
LO2: Defend the need for, and means of, obtaining high quality data in wildlife management and perform appropriate analyses on wildlife data.
LO3: Demonstrate ability to present data and analytical results clearly and concisely to a professional standard.
LO4: Competently derive management and further research recommendations based on the results from data analysis

You are required to provide answers to a series of questions that will be based directly or indirectly on material and skills learned during the three case-studies covered. All of the instructions required will be indicated on the assignment’s specific INSTRUCTION SHEET, which will be released at the beginning of the assignment period. Please read the instructions and each question carefully.

 The assignment MUST be completed alone. You are not entitled to work together on your analysis or answers for the assignments

Use of Gen AI is not permitted in writing your answers or coding. This is because we (i) expect you to read around the topics, apply your own reasoning and ultimately come away with a deeper understanding and (ii) regarding coding, previous students who have used AI have tended to get the answers incorrect and not understand why or what the code is doing. You should apply as far as possible the approaches taught in the Module and MSc.

You will need to declare any use of Gen AI tools, or declare that you have not, via an assessment declaration before submission.

You are expected to save a dated version of your assessment each day that you work on it in the format TITLE YYYY.MM.DD. For example, if your work is called ‘Take home exam’ and you do some work on it on 29th October 2026 you should save a copy called ‘Take home exam 2026.10.29’. Please also save any additional files you produce, such as R coding scripts, in this manner. Do not edit them after saving them so that the metadata shows the last edited date. These do not have to be submitted, but if your work has signs of use of Gen AI you may be asked to share these dated copies to show the progression of your work.

Deadline for Submission 

Sunday 14th December 2025 – 23:59h

Your attention is drawn to the penalties for late submissions:  All coursework, both at first attempt and reassessment, submitted after the agreed deadline will be marked at a maximum of 50 per cent. Coursework submitted over five working days after the agreed deadline will be given a mark of zero per cent, although formative feedback will be offered to the student where requested, as per Academic Regulations Sections B and C.

Submission Method

Submission will be via a Turnitin portal on the module’s Moodle page.

 For every assessment, complete a Mandatory Declaration which must confirm that the submission is your own and has not been submitted for another assessment and a statement about any (or no) use of Gen AI. Follow the instructions on Moodle - this is done via an online question that then allows submission.

 You are advised to keep your own copy of the final assessment with your dated backups detailed above.

Feedback will be given via Turnitin. The marker will give an assessment of the extent to which you have met each assessment criteria (below), and provide any appropriate comments. If you have any difficulty in understanding the feedback you are given, then please contact the module leader.

 Note that the Assessment Policy (5.3) and Academic Regulations (A7.9) specify that students are entitled to feedback on coursework within fifteen working days (typically three full weeks) from date of submission

Assessment

Regulations & Academic Conduct