Recent Posts

Blog Post 7 for Katarina Duke

User:  | Open Learning Faculty Member: 


Theoretical basis of my research project is to demonstrate how algae growth varies depending on the flow regime of freshwater creeks. The growth of algae can act as an indicator for water quality (i.e. water pollution) and as a predictor for the maintenance of water supply systems (i.e. intake pipes and filter lines). Algae can deplete the oxygen in water, release toxins, and lead to taste and odour issues. More turbulence leads to more oxygen absorbed by water, thus counteracting the oxygen depleted by algae. The creeks sampled in my research project have proven to be fish bearing through previously conducted fish presence studies and observation. Establishing the connection between algae growth and flow regimes within freshwater creeks will aid in maintaining a healthy ecosystem for fish and predict creeks potentially at risk for loss of fish.

Ecological processes that my hypothesis will touch on are the hydrologic cycle and nutrient cycling.

It is also important to acknowledge that temperature, seasonality, weather, and unknown anthropogenic activities can affect the growth rate of algae. Other studies have been completed focusing on the relationship between nutrient levels and algae growth as well as temperature and algae growth.

Keywords: Algae growth, flow regime, water depth, turbulent

Blog Post 6 for Katarina Duke

User:  | Open Learning Faculty Member: 


On average 20-25 flow measurements were taken within each creek to determine the flow discharge with 5 discharge measurements obtained per creek for a total of 25 discharge measurements. No issues occurred in implementing the sample design due to the resources available; the SonoTek Flow Tracker 2 helps eliminates sampling issues by providing quality control errors for flow measurements taken within the creek to help prevent flow measurement errors form compounding onto the discharge measurement.

Preparation for data collection was minimal as the only supplies needed in the field was a journal, SonoTek Flow Tracker 2, and spare batteries. I was not required to make tables prior to arriving in the field as the SonoTek Flow Tracker 2 stores all data that can then be downloaded onto a computer.

It has been observed that despite all the creeks being hydraulically connected to the Miracle Valley aquifer the drops in flow rate into low season of each of the creeks did not happen at the same rate. This allows me to infer that the creeks are hydraulically connected to the aquifer to varying degrees.
I also observed the presence of a fish farm in the area, west of Belcharton Creek before its confluence with Lagace Creek This did raise concerns that the algal growth observed within the creeks could be a result of excess nutrients from the farms; however, algal growth was more prominent in Seux Brook that is not within proximity of potential discharge from the fish farm. Also, since no algal growth was observed in Lagace Creek my concerns were absolved since Belcharton is a tributary of Lagace Creek.

Blog Post 5 for Katarina Duke

User:  | Open Learning Faculty Member: 


The only real challenge that presented itself was finding a viable location within each creek to perform discharge measurements. The device used for obtaining the discharge measurements requires a fairly cobble-free creek bed, no obstructions to flow within the creek (i.e. logs), and a visually uniform flow (i.e. avoid switchbacks). This proved to be challenge for Durieu Creek and Legace Creek as they experience a great deal of sediment deposition. Durieu Creek also follows a steep terrain with many switchbacks causing many sections of the creek to be unsuitable for obtaining measurements. Selection of the creeks for the study was chosen systematically based on their hydraulic connection with the Miracle Valley aquifer. Locations for sampling along the creeks were conducted using random sample selection. I intend to collect data using the same technique as it is the method defined in the Manual of British Columbia Hydrometric Standards and Environment Canada’s “A Field Guide to Streamflow Measurement by Gauging and Metering.” I will also continue to use the SonoTek Flow Tracker 2 as it is widely used by water resource professionals and provides quality control warning messages to assist with data accuracy. The location of measurements will not be changed as a base point is required to determine changes in water depth. Also differences in creek bed composition (i.e. amount of cobbles) can affect flow measurements. By using the same locations and equipment the influence of confounding variables can be controlled.

 

 

Response post was completed for:

Blog Post 3: Ongoing Field Observations

Blog Post 3 for Katarina Duke

User:  | Open Learning Faculty Member: 


I intend to study the relationship between abiotic factors and algal growth in freshwater streams within the Miracle Valley. The study will be focused on the impact water depth and flow regime have on algal growth.

Five creek locations within the Miracle Valley were sampled on May 31, 2018 and June 1, 2018 for flow discharge rates and water depth. The results of the measurements are shown below in the table below.

Additional observations from the site visit include are as shown in pages from field book.

July 5 Field Notes Page 1 July 5 Field Notes Page 2 June 1 Field Notes June 19 Field Notes Page 1 June 19 Field Notes Page 2 May 15 Field Notes Page 1 May 15 Field Notes Page 2 May 15 Field Notes page 3 May 31 Field Notes

I hypothesize that a relationship exists between algal growth and flow regime and water depths. I predict that as water depths decrease, provided creeks are not intermittent (i.e. run dry depending on seasonality) algal growth will increase and turbulent waters will reduce algal growth.

The hypothesis will be tested using a SonoTek Flow Tracker 2 to measure flow discharge rates at same location each site visit.  Rebar will be used as a staff gauge to measure difference in water level before and after discharge measurements and to compare with measurements taken on other days. A scale of none-to-moderate-to significant will be used to estimate algal growth.

The experiment will be conducted using logistic regression and the hypothesis is that slower flow regimes and shallow water depths increase algal growth.

In this experiment the response variable is algal growth that is categorical (using a scale of none, minimal, moderate, significant, very significant). The explanatory variables are continuous and include the flow rate and water depth.

Blog Post 1 for Katarina Duke

User:  | Open Learning Faculty Member: 


The study location is known as Miracle Valley, sometimes also referred to as Upper Hatzic Valley that extends from Lagace Creek at the south to the Stave Lake reservoir at the north. The valley is bounded by steep mountainous terrain to the east and west.

Land use in the area is predominantly forest, accompanied by rural residential and low-intensity agricultural uses. Ground elevations within the area rise abruptly north of Durieu Road and then plateau and begin to decline at Stave Lake.

As per data from the nearest climate monitoring station—courtesy of Environment Canada—the area receives approximately 1,808mm of precipitation annually with approximately 68% of the total annual precipitation falls between the months of October and March (Government of Canada, 2018).

The watercourses included in the study include Lagace Creek that crosses the south end of the Valley that obtains flows from tributaries to the west. (i.e. Belcharton Creek, Durieu Creek, Oru Creek, and Seux Brook). The tributary creeks are incised into steep-sided ravines and are declared to be largely spring-fed, due to relatively constant flows throughout the year (Piteau Associates Engineering Ltd., 2012).

A reconnaissance site visit was completed on May 15, 2018 from 08:00 to 15:35. The weather was predominantly sunny with about 20% cloud cover and a temperature ranging from approximately 9 to 21 degrees Celsius throughout the day.

Map of Sampling Locations

Observations:

  • Note: Groundwater fed creeks
  • algal growth observed in Seux Brook in abundance prior to confluence of Seux Brook and Oru Creek
  • No algal growth observed in Legace Creek, Belcharton Creek, Oru Creek or Durieu Creek
  • No moss present on trees within immediate proximity of Legace Creek
  • Significant stream bank vegetation growth observed on Belcharton Creek
  • Heavy amounts of moss present on trees surrounding Durieu Creek
  • Clusters of moss present on trees surrounding Belcharton Creek
  • Variety of avian species and squirrels observed around Oru Creek
  • Water seepage observed in area around Durieu Creek.
  • Grasses growing in and along stream bank of Seux Brook
  • Flow discharges (highest to lowest) Legace, Belcharton, Oru, Durieu, Seux
  • Durieu Creek and Legace Creek bed heavily cobbled, turbulent flows
  • Seux Brook and Oru Creek observed relatively laminar flows
  • Seux Brook bed silt and clay
  • Seux Brook is partially shaded prior to confluence with Oru Creek
  • Oru Creek is not shaded at all
  • Durieu Creek is heavily shaded
  • Belcharton and Legace Creek are shaded in sections throughout water course

May 15 Field Notes Page 1 May 15 Field Notes Page 2 May 15 Field Notes page 3

Questions that could be proposed from the observation is:

  1. Is the location of moss growth on trees an accurate way to determine cardinal direction?
  2. Does water depth influence algal/vegetation growth?
  3. Does flow regime influence algal/vegetation growth?
  4. Does the extent of cobbles within creek beds influence algal/vegetation growth?
  5. How does the weather (i.e. sunlight and temperature) influence algal/vegetation growth?
  6. Does the flow regime and water depth change due to seasonality and weather?

PHOTOS:

Katarina Duke- May 15 Photos

REFERENCES:

Government of Canada. (2018, April 24). Station Results – Historical Data. Retrieved from Government of Canada: http://climate.weather.gc.ca/historical_data/search_historic_data_stations_e.html?StationID=702&Day=20&timeframe=1&type=line&MeasTypeID=dptemp&Month=7&Year=2016&searchType=stnProx&txtRadius=25&optProxType=navLink&txtLatDecDeg=49.025277777778&txtLongDecDeg

Piteau Associates Engineering Ltd. (2012). MISSION HYDROGEOLOGICAL INVESTIGATION FOR GROUNDWATER SUPPLY MIRACLE VALLEY, B.C. North Vancouver: Piteau Associates Engineering Ltd.

 

Blog Post 4 for Katarina Duke

User:  | Open Learning Faculty Member: 


Three sampling methods were used in gathering data from the Mohn Mill community using the virtual forest tutorial: haphazard, random, and systematic.

An equal number of quadrats were sampled (i.e. 30 each) with the systematic sampling technique having the fastest sampling time but, the sampling time for all three methods remained within the range of 15 to 16 hours. The haphazard method had a sampling time of 15 hours and 57 minutes, and the random sampling method has a sample time of 15 hours and 49 minutes.

In all three sampling strategies, Red Maple and White Oak were determined to be the two most common species; However, the results for the two rarest species differed for each method (i.e. American Basswood, Sweet Birch, White Ash, and Hawthorn).

  1. Haphazard or convenience sampling

Using the area, haphazard sampling technique for the Mohn Mill community, American basswood and Hawthorn were the two rarest species as indicated by the actual importance value.

·         Hawthorn

Actual importance value: 0.6

Calculated importance value: 0.4

 

·         Sweet birch

Actual importance value:  0.2

Calculated importance value: 0.7

 

 

  1. Random sampling

 

Using the area, random sampling technique for the Mohn Mill community, White ash and Hawthorn were the two rarest species as indicated by the actual importance value.

 

·         White Ash

Actual importance value: 0.2

Calculated importance value: 0.6

 

·         Hawthorn

Actual importance value: 0.6

Calculated importance value: 0.6

 

 

 

 

  1. Systematic sampling

 

Using the area, systematic sampling technique for the Mohn Mill community, American basswood and Sweet birch were the two rarest species as indicated by the actual importance value.

·         American basswood:

Actual importance value: 0.2

Calculated importance value: 1.5

 

·         Sweet birch:

Actual importance value: 0.2

Calculated importance value: 0.7

 

For all three sampling methods—haphazard, random, and systematic sampling—the accuracy improved with abundance.

Of the three methods, the random sampling method had the highest accuracy.

I found it interesting that the systematic method of sampling had skewed the density of the rare species to such a substantial extent, making a haphazard sampling approach appear to be a more desirable sampling method. I was also surprised to see haphazard having the degree of accuracy it did.

A reason for the lack of accuracy using the systematic sampling method could potentially be using a transect sampling method in conjunction with the systematic method. I selected the samples at regular distances along the transect, with the initial point randomly chosen. As stated in “Tutorial: Sampling techniques,” systematic sampling can produce problems if the points correspond to an underlying environmental pattern, which perhaps is the case for Mohn Mill community.

I am curious about the results stratified sampling and transects would obtain. For stratified sampling, the tree population would be split into somewhat homogenous groups (same species). I predict the accuracy for stratified sampling would be equivalent to, if not better than, the accuracy of random sampling and that the common species determined would match. I think stratified sampling would determine the rarest species to be Hawthorn and Sweet birch due to their occurrence in two of sampling methods used.

A method I am aware of that is commonly used in the forestry industry is the point-centered quarter method, where a point in the center of the forest is identified and then the area surrounding it is separated into four quarters. I am surprised this method was not within the tutorial given its common use in relation to trees. I’d be interested in seeing how the method compares to those used within the tutorial in terms of the rare species determined and accuracy.

 

Blog Post 2 for Katarina Duke

User:  | Open Learning Faculty Member: 


A source of ecological information is the International Journal of Ecology which can be found by clicking the URL link provided below.

https://www.hindawi.com/journals/ijecol/

A peer-reviewed research article requires the works to be written by an expert in the field, include in-text citation, contain a bibliography, be reviewed by a referee prior to publication, and report methods and results of a field study.

An article that interested me and meets the requirements to be classified as a peer-reviewed research article is “Benthic Macroinvertebrates Diversity as Bioindicator of Water Quality of Some Rivers in East Kalimantan, Indonesia.” (Refer to attachment for research article)

The objective of the research was to clarify and evaluate the water quality of several rivers in East Kalimantan province of Indonesia by utilizing the benthic macroinvertebrates diversity and physical-chemical parameters of the river water.

The research article was written by Fatmawati Patang, Agoes Soegianto, and Sucipto Hariyanto belonging to the Department of Biology, Faculty of Sciences and Technology at the Universitas Airlangga. All experts in the field of water quality, biology, and toxicology. For instance, Agoes Soegianto is a PhD recipient and professor at Universitas Airlangga. He is an expert in marine biology, environmental contamination, and ecotoxicology, having completed over 40 research works in his field.

The article includes in-text citation accompanied by a bibliography and was reviewed by Panos V. Petrakis prior to publication. Petrakis is associated with the Institute for Mediterranean Forest Ecosystems as a research scientist. Petrakis’ expertise in ecology and biodiversity makes him a suitable candidate for referee for the research article.

The source also reports the result of a field study completed by the authors who include in the article their method and results. Their method included sampling a minimum of 100 individual benthic macroinvertebrates and the measurement of numerous water quality parameters (i.e. dissolved oxygen, biological oxygen demand, pH, turbidity, etc.). The macroinvertebrates were identified and enumerated to calculate the diversity, dominance and evenness index as they are frequently used to predict the conditions of aquatic environments. The results supported their hypothesis that the Karang Mumus River recently received pollutants that could be classified as dangerous.

To further support the classification of the article as peer-reviewed research material, the article is offered though the International Journal of Ecology that operates as a peer-reviewed open-access journal.

Blog Post 4: Sampling Strategies

User:  | Open Learning Faculty Member: 


For this assignment, I was required to use the systematic sampling method, the random sampling method and also the haphazard sampling method in the virtual forest tutorial. The sampling technique with the fastest estimate sampling time ended up being the systematic sampling method with a time of 12 hours and 34 minutes. The next fastest sampling time was the random sampling method at 12 hours and 41 minutes, and the slowest sampling technique ended up being the haphazard method with a time of 12 hours and 49 minutes.

The two most common species were: Red Maple and Witch Hazel

The percentage error of the two most common species:

Systematic:

Red Maple= 9.8% error

Witch Hazel= 12.2% error

Random:

Red Maple= 22.4% error

Witch Hazel= 48.2% error

Haphazard:

Red Maple= 13.1% error

Witch Hazel= 33.3% error

 

The two rarest species were: Sweet Birch and Hawthorn

The percentage error of the two rarest species:

Systematic:

Sweet Birch = 82.7% error

Hawthorn = 14.9% error

Random:

Sweet Birch = 24.4% error

Hawthorn = 62.4% error

Haphazard:

Sweet Birch = 55.7% error

Hawthorn = 71.1% error

 

From the results, on average the trend seems to be that the accuracy increases when there is a greater species abundance, in other words the greater the species abundance the less of a percentage error there was.

Systematic sampling method percentage error (more accurate on average): 29.9%

Random sampling method percentage error: 39.35%

Haphazard method percentage error: 43.3%

So, as you can see the systematic sampling method has the lowest percentage error and therefore, is on average the most accurate.

 

Blog Post 5: Design Reflections

User:  | Open Learning Faculty Member: 


From observations gathered from the Alfred Howe Greenway, Port Moody, BC, in Blog Post 1 and Blog Post 3, when walking along from the south end of the trail (elevation: 118 m) to the north end of the trail (elevation: 50m), there appeared to be a change in pine tree density.

A stratified random sampling strategy was used to measure pine tree density along three points of an elevation gradient using the nearest individual method to select each sampling unit. Along each elevation category (A. 120-110 m, B. 90-80 m, C. 60-50 m) five sampling units were selected by generating a random compass bearing and number of paces using an app. From the randomly generated location, the distance from the nearest pine tree to its neighbour was measured.

Although using the nearest individual method to select the sampling unit was more efficient than formulating a coordinate grid overlaying a map of the area in order to select a specific sample quadrant, a few difficulties were encountered in implementing and interpreting data collected using the nearest individual method, stratified random sampling strategy. When interpreting the results of this particular sampling strategy, the pine tree density of a particular elevation category is measured as the average distance from one pine tree to the next, which would (1) record the upper limit of the pine tree density for that particular area, as there would be a disregard for spaces where there is a significant lack of pine trees. This would particularly effect data in areas where it was observed that pine trees were found in “patches” rather than having a more uniform distribution. Furthermore, when implementing the sampling strategy, due to the steep elevation gradient, (2) difficulties were encountered to generate a random bearing that would generate a sampling location within the desired elevation category. If the same technique is used for a future data collection, perhaps the range for the number of paces should be decreased.

The data collected (although having a sample size of less than 10 measurements for each elevation category) represented a linear decrease in the average pine tree density along the elevation gradient. Although this result supports an initial hypothesis of pine tree density decreasing along the elevation gradient, the difficulties encountered (mentioned above) in interpreting the data brings the accuracy of the result to question (ex. perhaps there might be an exponential decline).

It would be favourable to continue to build future research of the Alfred Howe Greenway around pine tree data collection, as they seem to be found along the entire trail, in contrast to other plant species that only appear at one point of the trail or are seasonal. Perhaps a modified approach of a point-centered quarter method will be used for measuring pine tree species density in order to attain more accurate pine tree density measurements. By using the point-centered method, the distance of each pine tree (with the average interpreted as the pine tree density for a particular elevation category) would be recorded as a measurement of the nearest individual from each quarter to the centre point of the quadrant. As a result, recordings of solely the upper density limits will be avoided.


For the second portion of this blog post I commented on kmcara’s Blog Post 3: Ongoing Observations .


EDIT: From researching for archives about the Alfred Howe Greenway on the Port Moody, BC, Government website, it was found that the north point of the trail encompasses an area formerly used as a landfill site from the 1950’s to 1982, in addition to being used for green waste up until 2002. (City of Port Moody, 2018; Payne, 2015)

This offers a clear explanation for the significantly contrasting abundance of pine trees (and overall species diversity) between the north point (formerly a landfill) and south point (historically forested area) of the trail.

As a result of this newly found information I will perhaps shift my area of study to observe any current impact the former landfill has on the surrounding ecosystem.

Blog Post 4: Sampling Strategies

User:  | Open Learning Faculty Member: 


Three sampling strategies, inclusive of systematic, random, and haphazard, were used in the virtual sampling tutorial. The technique with the fastest estimated sampling time was systematic sampling (12 hours and 31 minutes).

Eastern hemlock and sweet birch were the two most common species in this tutorial. Systematic sampling yielded the lowest percent error for eastern hemlock (1.98%), however, percent error for sweet birch was lowest when the haphazard sampling technique was applied (17%).

Eastern Hemlock: Systematic = 1.98% error, Random = 32.3% error, Haphazard = 10.8% error

Sweet Birch: Systematic = 36.17% error, Random = 46.21% error, Haphazard = 17% error

Overall, when comparing percent error results for the two most common species, haphazard sampling had the lowest overall average percent error (13.9%), compared to systematic sampling (19.1%) and random sampling (39.3%).

The random sampling strategy proved to be the most accurate technique for the two rarest species: striped maple and white pine. Percent error for striped maple using the random sampling technique was still quite high, despite having the lowest error of all the techniques applied.

Striped maple: Systematic = 76% error, Random = 50.9% error, Haphazard = 114% error

White pine: Systematic = 100% error, Random = 2.4% error, Haphazard = 100% error

Random sampling, on average, was the most accurate technique when used to sample the rarest species (26.7% error), as compared to systematic sampling (88% error) and haphazard sampling (107% error).

Overall, greater species abundance led to greater accuracy.