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Information
| Code | ST0030 |
| Name | Applied Numerical Ecology-1 |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 2-2 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
Numerical ecology is the subdiscipline that investigates communities and assemblages using quantitative methods. This course will develop the skills in analyzing and interpreting the structure and biodiversity of ecological communities as well as investigating its correlations with environmental conditions. After theoretical background and assumptions of the methods are shortly addressed, applications will be performed using R library “vegan” and graphical user interface “BiodiversityR”. Basic knowledge on the ecology, statistics and hypothesis testing are required for attending the class. Necessary R skills will be introduced during the course.
Course Content
Basic R usage, community, assemblage and habitat concepts, basics of ecological data, preparing community and environment matrices, biodiversity, richness, evenness, dominance and rarity concepts, similarity of species composition, similarity matrices, similarity indices, cluster analyses, Simprof analyze, unconstrained ordination techniques, princple component analyze (PCA), multidimensional scaling (MDS), non-metric multidimensional scaling (NMDS), correspondance analyze (CA), biodiversity profiles, dominant species, characteristic species, rank-abundance curves, Simper analyze, Analyzing effect of environmental conditions on assemblages, Anosim, Permanova, constrained ordination techniques, redundancy analyze (RDA), constrained (canonical) correspondance analyze (CCA), constrained analyze of principle coordinates (CAP), model selection and validation procedures, results presentation in constrained ordination analyzes.
Course Precondition
Successful completion of a lecture on introductory level statistics and hypothesis testing, successful completion of a lecture on general ecology.
Resources
Borcard D, Gillet F, Legendre P. 2011. Numerical Ecology with R. Kindt R, Coe R. 2005. Tree diversity analysis: A manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre: Nairobi, Kenya. Magurran A. 2004. Measuring biological diversity. Odum EP, Barrett, GW, 2008. General Principles of Ecology.
Notes
Clarke KR, Somerfield PJ, Gorley RN. 2008. Testing of null hypotheses in exploratory community analyses: similarity profiles and biota-environment linkage. Journal of Experimental Marine Biology and Ecology 366: 56–69.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Gain basic R usage skills |
| LO02 | Gain knowledge on the basics of community ecology |
| LO03 | Quantitatively expresses the biodiversity, richness, dominance and evenness concepts |
| LO04 | Analyze the composition of ecological communities and identifies different assemblages |
| LO05 | Make a selection among different clustering and ordination methods |
| LO06 | Define indicator species of communities using quantitative methods |
| LO07 | Examine the correlations between environmental conditions and communities. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Improves theoretical and practical knowledge in the field of Marine and Inland Water Biology and Fisheries Basic Sciences. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Comprehends interactions between Fisheries Basic Sciences and other disciplines. | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Determines strategies and investigates methods about their field of study in Fisheries Basic Science. | 2 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Produces new information and theories by interpreting and synthesising the information from other disciplines and uses the theoretical and practical information from their field of study in Fisheries Basic Science. | 2 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Collects data, interprets results and suggests solutions by using dialectic research methodology in the certain field of Marine and Inland Water Biology and Fisheries Basic Sciences. | 5 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Independently plans, designs and performs a certain project in the field of Fisheries Basic Sciences. | 4 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Produces solutions by improving new strategic approaches and taking responsibilities for the potential problems in the field of study as an individual or team member. | 4 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Determines the requirements for Fishery Basic Science education, reaches the resources, critically interpretes knowledge and skills and gains experience to direct the education. | 3 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Has positive stance on the lifelong education and uses it for the public benefit by using the gained theoretical and practical knowledge in the field of Marine and Inland Water Biology and Fisheries Basic Sciences. | |
| PLO10 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Follows the current topics and improvements in the field of Fisheries Basic Sciences, publishes and presents the research results, contributes to constitution of a public conscience in the field of interest. | |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Effectively communicates about the field of Marine and Inland Water Biology and Fisheries Basic Sciences by using written and oral presentation tools, follows up and criticizes the meetings and seminars. | 3 |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Follows up international publications and communicates with international collaborators by using language skills. | 3 |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Uses the communication and information technologies about the field of interest in an advanced level. | 5 |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | Conforms, controls and teaches social, cultural and scientific ethics in the investigation and publication process of the data related with the field of interest. | 5 |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | Improves strategies, politics and application codes by following scientific and technological developments on the certain field of Marine and Inland Water Biology and Fisheries Basic Sciences. Investigates and extends the results on behalf of public in frame of total quality management process. | 2 |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | Uses the abilities and experiences on applications and solving problems that gained during the MSc education for the interdisciplinary studies. | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Basics of R environment and graphical user interfaces (GUI) | Attendants should install R Terminal, and R Studio and R Commander GUIs | Öğretim Yöntemleri: Anlatım, Problem Çözme, Alıştırma ve Uygulama |
| 2 | Community, assemblage and habitat concepts | Odum ve Barrett, 2008 sf 282-336 | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Basics of ecological data, preparing community and environment matrices | Kindt and Coe, 2005 sf 19-39; Borcard et al., 2011 sf 1-53 | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme, Proje Temelli Öğrenme |
| 4 | Richness, biodiversity, evenness and dominance | Kindt ve Coe, 2005 sf 39-71; Magurran, 2005 -sf 1-17 | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme, Proje Temelli Öğrenme |
| 5 | Similarity of species composition, similarity matrices | Kindt ve Coe, 2005 sf 123-139 | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme, Proje Temelli Öğrenme |
| 6 | Identifying assemblages with cluster and Simprof analyses | Kindt ve Coe, 2005 sf 139-153; Borcard ve ark., 2011 sf 53-115, Clarke ve ark., 2008 | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme, Proje Temelli Öğrenme |
| 7 | Identifying assemblages with unconstrained ordination techniques | Kindt ve Coe, 2005 sf 153-171; Borcard ve ark., 2011 sf 115-149 | Öğretim Yöntemleri: Anlatım, Problem Çözme, Bireysel Çalışma |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Ödev |
|
| 9 | Characteristics of assemblages (biodiversity profiles) | Kindt ve Coe, 2005 sf 39-71 | Öğretim Yöntemleri: Anlatım, Problem Çözme, Bireysel Çalışma |
| 10 | Defining dominant and characteristic species (Rank abundance curves, Simper analyze) | Kindt ve Coe, 2005 sf 39-71; Magurran, 2005 sf 18-71 | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 11 | Analyzing effect of environmental conditions on assemblages-1 (Anosim and Permanova) | Kindt ve Coe, 2005 sf 171-196; Borcard ve ark., 2011 sf 153-224 | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 12 | Analyzing effect of environmental conditions on assemblages-2 (Constrained ordinations, RDA, CCA, CAP) | Kindt ve Coe, 2005 sf 171-196; Borcard ve ark., 2011 sf 153-224 | Öğretim Yöntemleri: Anlatım, Proje Temelli Öğrenme , Problem Çözme |
| 13 | Model selection and validation procedures and results presentation in constrained ordination analyzes | Borcard ve ark., 2011 sf 153-224 | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 14 | Sample Application | Sample data will be obtained | Öğretim Yöntemleri: Proje Temelli Öğrenme |
| 15 | Sample Application-2 | Sample data will be obtained | Öğretim Yöntemleri: Proje Temelli Öğrenme |
| 16 | Term Exams | Ölçme Yöntemleri: Performans Değerlendirmesi |
|
| 17 | Term Exams | Ölçme Yöntemleri: Performans Değerlendirmesi |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 2 | 2 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 28 | 28 |
| Total Workload (Hour) | 154 | ||
| Total Workload / 25 (h) | 6,16 | ||
| ECTS | 6 ECTS | ||