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Labour Force Estimates by Industry and by Occupation (2 and 3 Digits) for Selected Alberta Economic Regions (Annual Average) (1987 - 2011)


Year: 2015

Abstract:
(StatCan Product) Customization details:  This information product has been customized to present information on labour force estimates by industry and by occupation (2 and 3 digits) for Alberta’s Economic Regions (excluding Edmonton and Calgary) from 1987 to 2011 (annual averages).  The LFS characteristics presented are:  - Labour Force     - Employment  The Economic Regions presented are:  - Lethbridge – Medicine Hat - Camrose – Drumheller - Banff- Jasper – Rocky Mountain House - Red Deer - Athabasca – Grande Prairie – Peace River - Wood Buffalo – Cold Lake   Labour Force Survey  The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these.  Target population  The LFS covers the civilian, non-institutionalized population 15 years of age and over. It is conducted nationwide, in both the provinces and the territories. Excluded from the survey's coverage are: persons living on reserves and other Aboriginal settlements in the provinces; full-time members of the Canadian Armed Forces and the institutionalized population. These groups together represent an exclusion of less than 2% of the Canadian population aged 15 and over. National Labour Force Survey estimates are derived using the results of the LFS in the provinces. Territorial LFS results are not included in the national estimates, but are published separately.  Instrument design  The current LFS questionnaire was introduced in 1997. At that time, significant changes were made to the questionnaire in order to address existing data gaps, improve data quality and make more use of the power of Computer Assisted Interviewing (CAI). The changes incorporated included the addition of many new questions. For example, questions were added to collect information about wage rates, union status, job permanency and workplace size for the main job of currently employed employees. Other additions included new questions to collect information about hirings and separations, and expanded response category lists that split existing codes into more detailed categories.  Sampling  This is a sample survey with a cross-sectional design.  Data sources  Responding to this survey is mandatory. Data are collected directly from survey respondents. Data collection for the LFS is carried out each month during the week following the LFS reference week. The reference week is normally the week containing the 15th day of the month. LFS interviews are conducted by telephone by interviewers working out of a regional office CATI (Computer Assisted Telephone Interviews) site or by personal visit from a field interviewer. Since 2004, dwellings new to the sample in urban areas are contacted by telephone if the telephone number is available from administrative files, otherwise the dwelling is contacted by a field interviewer. The interviewer first obtains socio-demographic information for each household member and then obtains labour force information for all members aged 15 and over who are not members of the regular armed forces. The majority of subsequent interviews are conducted by telephone. In subsequent monthly interviews the interviewer confirms the socio-demographic information collected in the first month and collects the labour force information for the current month. Persons aged 70 and over are not asked the labour force questions in subsequent interviews, but rather their labour force information is carried over from their first interview. In each dwelling, information about all household members is usually obtained from one knowledgeable household member. Such 'proxy' reporting, which accounts for approximately 65% of the information collected, is used to avoid the high cost and extended time requirements that would be involved in repeat visits or calls necessary to obtain information directly from each respondent.  Error detection  The LFS CAI questionnaire incorporates many features that serve to maximize the quality of the data collected. There are many edits built into the CAI questionnaire to compare the entered data against unusual values, as well as to check for logical inconsistencies. Whenever an edit fails, the interviewer is prompted to correct the information (with the help of the respondent when necessary). For most edit failures the interviewer has the ability to override the edit failure if they cannot resolve the apparent discrepancy. As well, for most questions the interviewer has the ability to enter a response of Don't Know or Refused if the respondent does not answer the question. Once the data is received back at head office an extensive series of processing steps is undertaken to thoroughly verify each record received. This includes the coding of industry and occupation information and the review of interviewer entered notes. The editing and imputation phases of processing involve the identification of logically inconsistent or missing information items, and the correction of such conditions. Since the true value of each entry on the questionnaire is not known, the identification of errors can be done only through recognition of obvious inconsistencies (for example, a 15 year-old respondent who is recorded as having last worked in 1940).  Estimation  The final step in the processing of LFS data is the assignment of a weight to each individual record. This process involves several steps. Each record has an initial weight that corresponds to the inverse of the probability of selection. Adjustments are made to this weight to account for non-response that cannot be handled through imputation. In the final weighting step all of the record weights are adjusted so that the aggregate totals will match with independently derived population estimates for various age-sex groups by province and major sub-provincial areas. One feature of the LFS weighting process is that all individuals within a dwelling are assigned the same weight. In January 2000, the LFS introduced a new estimation method called Regression Composite Estimation. This new method was used to re-base all historical LFS data. It is described in the research paper ""Improvements to the Labour Force Survey (LFS)"", Catalogue no. 71F0031X. Additional improvements are introduced over time; they are described in different issues of the same publication.  Data accuracy  Since the LFS is a sample survey, all LFS estimates are subject to both sampling error and non-sampling errors. Non-sampling errors can arise at any stage of the collection and processing of the survey data. These include coverage errors, non-response errors, response errors, interviewer errors, coding errors and other types of processing errors. Non-response to the LFS tends to average about 10% of eligible households. Interviews are instructed to make all reasonable attempts to obtain LFS interviews with members of eligible households. Each month, after all attempts to obtain interviews have been made, a small number of non-responding households remain. For households non-responding to the LFS, a weight adjustment is applied to account for non-responding households. Sampling errors associated with survey estimates are measured using coefficients of variation for LFS estimates as a function of the size of the estimate and the geographic area.

Labour Force Estimates by Industry and by Occupation (2 and 3 digits) for Selected Alberta Economic Regions (Annual Average) (2000-2013)


Year: 2015

Abstract:
(StatCan Product) Customization details:  This information product has been customized to present information on labour force estimates by industry and by occupation (2 and 3 digits) for Alberta’s Economic Regions (excluding Edmonton and Calgary) from 2000 to 2013 using annual averages.  The LFS characteristics presented are:  - Labour Force     - Employment  The economic regions presented are:  - Lethbridge – Medicine Hat - Camrose – Drumheller - Banff- Jasper – Rocky Mountain House - Red Deer - Athabasca – Grande Prairie – Peace River - Wood Buffalo – Cold Lake   For more information about the occupations and industries presented, contact OSI.Support@gov.ab.ca  Labour Force Survey  The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these.  Target population  The LFS covers the civilian, non-institutionalized population 15 years of age and over. It is conducted nationwide, in both the provinces and the territories. Excluded from the survey's coverage are: persons living on reserves and other Aboriginal settlements in the provinces; full-time members of the Canadian Armed Forces and the institutionalized population. These groups together represent an exclusion of less than 2% of the Canadian population aged 15 and over. National Labour Force Survey estimates are derived using the results of the LFS in the provinces. Territorial LFS results are not included in the national estimates, but are published separately.  Documentation – Labour Force Survey  Instrument design  The current LFS questionnaire was introduced in 1997. At that time, significant changes were made to the questionnaire in order to address existing data gaps, improve data quality and make more use of the power of Computer Assisted Interviewing (CAI). The changes incorporated included the addition of many new questions. For example, questions were added to collect information about wage rates, union status, job permanency and workplace size for the main job of currently employed employees. Other additions included new questions to collect information about hirings and separations, and expanded response category lists that split existing codes into more detailed categories.  Sampling  This is a sample survey with a cross-sectional design.  Data sources  Responding to this survey is mandatory. Data are collected directly from survey respondents. Data collection for the LFS is carried out each month during the week following the LFS reference week. The reference week is normally the week containing the 15th day of the month. LFS interviews are conducted by telephone by interviewers working out of a regional office CATI (Computer Assisted Telephone Interviews) site or by personal visit from a field interviewer. Since 2004, dwellings new to the sample in urban areas are contacted by telephone if the telephone number is available from administrative files, otherwise the dwelling is contacted by a field interviewer. The interviewer first obtains socio-demographic information for each household member and then obtains labour force information for all members aged 15 and over who are not members of the regular armed forces. The majority of subsequent interviews are conducted by telephone. In subsequent monthly interviews the interviewer confirms the socio-demographic information collected in the first month and collects the labour force information for the current month. Persons aged 70 and over are not asked the labour force questions in subsequent interviews, but rather their labour force information is carried over from their first interview. In each dwelling, information about all household members is usually obtained from one knowledgeable household member. Such 'proxy' reporting, which accounts for approximately 65% of the information collected, is used to avoid the high cost and extended time requirements that would be involved in repeat visits or calls necessary to obtain information directly from each respondent.  Error detection  The LFS CAI questionnaire incorporates many features that serve to maximize the quality of the data collected. There are many edits built into the CAI questionnaire to compare the entered data against unusual values, as well as to check for logical inconsistencies. Whenever an edit fails, the interviewer is prompted to correct the information (with the help of the respondent when necessary). For most edit failures the interviewer has the ability to override the edit failure if they cannot resolve the apparent discrepancy. As well, for most questions the interviewer has the ability to enter a response of Don't Know or Refused if the respondent does not answer the question. Once the data is received back at head office an extensive series of processing steps is undertaken to thoroughly verify each record received. This includes the coding of industry and occupation information and the review of interviewer entered notes. The editing and imputation phases of processing involve the identification of logically inconsistent or missing information items, and the correction of such conditions. Since the true value of each entry on the questionnaire is not known, the identification of errors can be done only through recognition of obvious inconsistencies (for example, a 15 year-old respondent who is recorded as having last worked in 1940).  Estimation  The final step in the processing of LFS data is the assignment of a weight to each individual record. This process involves several steps. Each record has an initial weight that corresponds to the inverse of the probability of selection. Adjustments are made to this weight to account for non-response that cannot be handled through imputation. In the final weighting step all of the record weights are adjusted so that the aggregate totals will match with independently derived population estimates for various age-sex groups by province and major sub-provincial areas. One feature of the LFS weighting process is that all individuals within a dwelling are assigned the same weight. In January 2000, the LFS introduced a new estimation method called Regression Composite Estimation. This new method was used to re-base all historical LFS data. It is described in the research paper ""Improvements to the Labour Force Survey (LFS)"", Catalogue no. 71F0031X. Additional improvements are introduced over time; they are described in different issues of the same publication.  Data accuracy  Since the LFS is a sample survey, all LFS estimates are subject to both sampling error and non-sampling errors. Non-sampling errors can arise at any stage of the collection and processing of the survey data. These include coverage errors, non-response errors, response errors, interviewer errors, coding errors and other types of processing errors. Non-response to the LFS tends to average about 10% of eligible households. Interviews are instructed to make all reasonable attempts to obtain LFS interviews with members of eligible households. Each month, after all attempts to obtain interviews have been made, a small number of non-responding households remain. For households non-responding to the LFS, a weight adjustment is applied to account for non-responding households. Sampling errors associated with survey estimates are measured using coefficients of variation for LFS estimates as a function of the size of the estimate and the geographic area.

Lake Athabasca


Author(s): Rawson, D. S.

Year: 1947

Citation:
Rawson, D. S. (1947).  Lake Athabasca . Bull. Fish. Res. Board Can. 72, 69-85.

Lake whitefish spawning study below Vermilion Chutes on the Peace River, October, 1992


Author(s): Patalas, J. W.

Year: 1993

Abstract:
Report of a study designed to estimate the munber of lake whitefish and other species utilizing the study area (Vermillion chutes on the Peace River in northern Alberta) during the fall, to identify spawning areas and the time of spawn, and to evaluate the overall egg production and its significance in terms of recruitment to the fish populations in the Peace River and Lake Athabasca.

Land-use/Land-cover Classifications of the Cold Lake Oil Sands Area Derived from 2005 Landsat Imagery (Image data, Tiff format)


Year: 2009

Abstract:
The Cold Lake oil sands area, Township 56 to 69, Range 1 to 11, west of the 4th Meridian, falls within the Lower Athabasca Regional Plan (LARP). As part of Alberta's Land-use Framework, LARP was developed in 2012 to set the stage for robust growth, vibrant communities and a healthy environment within the region. One of its implementation objectives is to balance the economic development of oil sands and impacts on ecosystem and environment. This is to be achieved through enhanced science-based monitoring for improved characterization of the environment and collection of the information necessary to understand cumulative effects. For this digital data release, a land use and land cover classification dataset was derived from 2005 Landsat multispectral imagery for the Cold Lake Oil Sands area. The classification contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 5 - mixed developed areas, 6 - developed areas, 7- shoal, 8 - shrub land, 9 - grassland, 10 - agriculture areas, 11 - coniferous forest, 12 - broad leaf forest, 13 - mixed forest, and 14- fire scar. These categories can be used as baseline data for planning, managing and monitoring surface infrastructure needs and impacts.

Land-use/Land-cover Classifications of the Cold Lake Oil Sands Area Derived from 2006 Landsat Imagery (Image data, Tiff format)


Year: 2009

Abstract:
The Cold Lake oil sands area, Township 56 to 69, Range 1 to 11, west of the 4th Meridian, falls within the Lower Athabasca Regional Plan (LARP). As part of Alberta's Land-use Framework, LARP was developed in 2012 to set the stage for robust growth, vibrant communities and a healthy environment within the region. One of its implementation objectives is to balance the economic development of oil sands and impacts on ecosystem and environment. This is to be achieved through enhanced science-based monitoring for improved characterization of the environment and collection of the information necessary to understand cumulative effects. This land classification raster dataset is derived from 2006 Landsat imagery. It contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 5 - mixed developed areas, 6 - developed areas, 7- shoal, 8 - shrub land, 9 - grassland, 10 - agriculture areas, 11 - coniferous forest, 12 - broad leaf forest, 13 - mixed forest, and 14- fire scar.

Land-use/Land-cover Classifications of the Cold Lake Oil Sands Area Derived from 2007 Landsat Imagery (Image data, Tiff format)


Year: 2009

Abstract:
The Cold Lake oil sands area, Township 56 to 69, Range 1 to 11, west of the 4th Meridian, falls within the Lower Athabasca Regional Plan (LARP). As part of Alberta's Land-use Framework, LARP was developed in 2012 to set the stage for robust growth, vibrant communities and a healthy environment within the region. One of its implementation objectives is to balance the economic development of oil sands and impacts on ecosystem and environment. This is to be achieved through enhanced science-based monitoring for improved characterization of the environment and collection of the information necessary to understand cumulative effects. This land classification raster dataset is derived from 2007 Landsat imagery. It contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 5 - mixed developed areas, 6 - developed areas, 7- shoal, 8 - shrub land, 9 - grassland, 10 - agriculture areas, 11 - coniferous forest, 12 - broad leaf forest, 13 - mixed forest, and 14- fire scar.

Land-use/Land-cover Classifications of the Cold Lake Oil Sands Area Derived from 2008 Landsat Imagery (Image data, Tiff format)


Year: 2009

Abstract:
The Cold Lake oil sands area, Township 56 to 69, Range 1 to 11, west of the 4th Meridian, falls within the Lower Athabasca Regional Plan (LARP). As part of Alberta's Land-use Framework, LARP was developed in 2012 to set the stage for robust growth, vibrant communities and a healthy environment within the region. One of its implementation objectives is to balance the economic development of oil sands and impacts on ecosystem and environment. This is to be achieved through enhanced science-based monitoring for improved characterization of the environment and collection of the information necessary to understand cumulative effects. This land classification raster dataset is derived from 2008 Landsat imagery. It contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 5 - mixed developed areas, 6 - developed areas, 7- shoal, 8 - shrub land, 9 - grassland, 10 - agriculture areas, 11 - coniferous forest, 12 - broad leaf forest, 13 - mixed forest, and 14- fire scar.

Land-use/Land-cover Classifications of the Cold Lake Oil Sands Area Derived from 2009 Landsat Imagery (Image data, Tiff format)


Year: 2009

Abstract:
The Cold Lake oil sands area, Township 56 to 69, Range 1 to 11, west of the 4th Meridian, falls within the Lower Athabasca Regional Plan (LARP). As part of Alberta's Land-use Framework, LARP was developed in 2012 to set the stage for robust growth, vibrant communities and a healthy environment within the region. One of its implementation objectives is to balance the economic development of oil sands and impacts on ecosystem and environment. This is to be achieved through enhanced science-based monitoring for improved characterization of the environment and collection of the information necessary to understand cumulative effects. This land classification raster dataset is derived from 2009 Landsat imagery. It contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 5 - mixed developed areas, 6 - developed areas, 7- shoal, 8 - shrub land, 9 - grassland, 10 - agriculture areas, 11 - coniferous forest, 12 - broad leaf forest, 13 - mixed forest, and 14- fire scar.

Land-use/Land-cover Classifications of the Cold Lake Oil Sands Area Derived from 2010 Landsat Imagery (Image data, Tiff format)


Year: 2009

Abstract:
The Cold Lake oil sands area, Township 56 to 69, Range 1 to 11, west of the 4th Meridian, falls within the Lower Athabasca Regional Plan (LARP). As part of Alberta's Land-use Framework, LARP was developed in 2012 to set the stage for robust growth, vibrant communities and a healthy environment within the region. One of its implementation objectives is to balance the economic development of oil sands and impacts on ecosystem and environment. This is to be achieved through enhanced science-based monitoring for improved characterization of the environment and collection of the information necessary to understand cumulative effects. This land classification raster dataset is derived from 2010 Landsat imagery. It contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 5 - mixed developed areas, 6 - developed areas, 7- shoal, 8 - shrub land, 9 - grassland, 10 - agriculture areas, 11 - coniferous forest, 12 - broad leaf forest, 13 - mixed forest, and 14- fire scar.

Land-use/Land-cover Classifications of the Cold Lake Oil Sands Area Derived from 2011 Landsat Imagery (Image data, Tiff format)


Year: 2009

Abstract:
The Cold Lake oil sands area, Township 56 to 69, Range 1 to 11, west of the 4th Meridian, falls within the Lower Athabasca Regional Plan (LARP). As part of Alberta's Land-use Framework, LARP was developed in 2012 to set the stage for robust growth, vibrant communities and a healthy environment within the region. One of its implementation objectives is to balance the economic development of oil sands and impacts on ecosystem and environment. This is to be achieved through enhanced science-based monitoring for improved characterization of the environment and collection of the information necessary to understand cumulative effects. This land classification raster dataset is derived from 2011 Landsat imagery. It contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 5 - mixed developed areas, 6 - developed areas, 7- shoal, 8 - shrub land, 9 - grassland, 10 - agriculture areas, 11 - coniferous forest, 12 - broad leaf forest, 13 - mixed forest, and 14- fire scar.

Land-use/Land-cover Classifications of the Cold Lake Oil Sands Area Derived from 2012 Landsat Imagery (Image data, Tiff format)


Year: 2009

Abstract:
The Cold Lake oil sands area, Township 56 to 69, Range 1 to 11, west of the 4th Meridian, falls within the Lower Athabasca Regional Plan (LARP). As part of Alberta's Land-use Framework, LARP was developed in 2012 to set the stage for robust growth, vibrant communities and a healthy environment within the region. One of its implementation objectives is to balance the economic development of oil sands and impacts on ecosystem and environment. This is to be achieved through enhanced science-based monitoring for improved characterization of the environment and collection of the information necessary to understand cumulative effects. This land classification raster dataset is derived from 2012 Landsat imagery. It contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 5 - mixed developed areas, 6 - developed areas, 7- shoal, 8 - shrub land, 9 - grassland, 10 - agriculture areas, 11 - coniferous forest, 12 - broad leaf forest, 13 - mixed forest, and 14- fire scar.

Land-use/Land-cover Classifications of the Cold Lake Oil Sands Area Derived from 2013 Landsat Imagery (Image data, Tiff format)


Year: 2009

Abstract:
The Cold Lake oil sands area, Township 56 to 69, Range 1 to 11, west of the 4th Meridian, falls within the Lower Athabasca Regional Plan (LARP). As part of Alberta's Land-use Framework, LARP was developed in 2012 to set the stage for robust growth, vibrant communities and a healthy environment within the region. One of its implementation objectives is to balance the economic development of oil sands and impacts on ecosystem and environment. This is to be achieved through enhanced science-based monitoring for improved characterization of the environment and collection of the information necessary to understand cumulative effects. This land classification raster dataset is derived from 2013 Landsat imagery. It contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 5 - mixed developed areas, 6 - developed areas, 7- shoal, 8 - shrub land, 9 - grassland, 10 - agriculture areas, 11 - coniferous forest, 12 - broad leaf forest, 13 - mixed forest, and 14- fire scar.

Late Pleistocene stratigraphy and sedimentology of coarse clastic deposits in the central Canadian Rocky Mountains, Jasper


Author(s): Levson, V. M.

Year: 1995

Abstract:
Six informal lithostratigraphic units are recognized from newly mapped exposures of Late Quaternary deposits in the Jasper region. The oldest deposits consist mainly of locally derived, coarse-grained gravelly diamicton interpreted as large volume, episodic, unchannelled, debris flow deposits. The inferred paleo-environment is a cool temperate, alpine to subalpine alluvial fan setting. Radiocarbon dates indicate that fan sedimentation began prior to 48 ka and continued throughout the Middle Wisconsinan. A unit of stratified gravels and sands interpreted as braided stream glaciofluvial deposits gradationally overlie the paleofan sequences and are distinguished from them by better stratification, sorting, clast roundness and relatively high percentages of distally-derived clasts. Wood from this unit near Jasper town site indicates deposition about 29 ka. Glaciolacustrine sediments overlying this unit in several Front Range valleys, reflect the onset of glaciation in the Athabasca River valley and associated ice-damming of tributary valley streams. Both Rocky Mountain and Cordilleran glaciers advanced through the area, depositing a complex sequence of ice-marginal sediments, basal tills and supraglacial deposits. Several lines of evidence suggest that at the Late Wisconsinan maximum, glaciers flowed out of the Athabasca valley into the conjectural 'ice free' corridor and were deflected southeasterly along the mountain front by the Laurentide ice sheet. During deglaciation, sedimentation in the Athabasca valley was dominated by ice-marginal deposition of glaciofluvial sands and gravels and by paraglacial debris flows. Large medial moraines and debris septa that formed at the confluence of main valley glaciers, such as occupied the Miette and Athabasca valleys, probably led to stagnant ice in the valley centre and associated kame terrace development along the valley sides. A radiocarbon date on shells from a small ice-marginal lake near Pocahontas indicates that glaciers there were in retreat by about 12 ka and alpine glaciers in the region were at or near their present limits by 10 ka. Sedimentologic studies of alluvial fan deposits in the area indicate that fast moving, noncohesive debris flows are prevalent and that the bedrock geology of the fan source areas exerts a strong control on sedimentation patterns. The conclusion that high energy, low frequency, debris flow events are prevalent in the Main Ranges, whereas lower energy, higher frequency events are typical of the Front Ranges is of relevance to modern hazard studies and mitigation efforts in the Canadian Rocky Mountains.

Legacy of a half century of Athabasca oil sands development recorded by lake ecosystems


Year: 2013

Abstract:
The absence of well-executed environmental monitoring in the Athabasca oil sands (Alberta, Canada) has necessitated the use of indirect approaches to determine background conditions of freshwater ecosystems before development of one of the Earth’s largest energy deposits. Here, we use highly resolved lake sediment records to provide ecological context to ∼50 y of oil sands development and other environmental changes affecting lake ecosystems in the region. We show that polycyclic aromatic hydrocarbons (PAHs) within lake sediments, particularly C1-C4–alkylated PAHs, increased significantly after development of the bitumen resource began, followed by significant increases in dibenzothiophenes. Total PAH fluxes in the modern sediments of our six study lakes, including one site ∼90 km northwest of the major development area, are now ∼2.5–23 times greater than ∼1960 levels. PAH ratios indicate temporal shifts from primarily wood combustion to petrogenic sources that coincide with greater oil sands development. Canadian interim sediment quality guidelines for PAHs have been exceeded since the mid-1980s at the most impacted site. A paleoecological assessment of Daphnia shows that this sentinel zooplankter has not yet been negatively impacted by decades of high atmospheric PAH deposition. Rather, coincident with increases in PAHs, climate-induced shifts in aquatic primary production related to warmer and drier conditions are the primary environmental drivers producing marked daphniid shifts after ∼1960 to 1970. Because of the striking increase in PAHs, elevated primary production, and zooplankton changes, these oil sands lake ecosystems have entered new ecological states completely distinct from those of previous centuries.

Legacy of a half century of Athabasca oil sands development recorded by lake ecosystems


Year: 2013

Abstract:
The absence of well-executed environmental monitoring in the Athabasca oil sands (Alberta, Canada) has necessitated the use of indirect approaches to determine background conditions of fresh- water ecosystems before development of one of the Earth’s largest energy deposits. Here, we use highly resolved lake sediment records to provide ecological context to ∼50 y of oil sands development and other environmental changes affecting lake ecosystems in the re- gion. We show that polycyclic aromatic hydrocarbons (PAHs) within lake sediments, particularly C1-C4–alkylated PAHs, increased signif- icantly after development of the bitumen resource began, followed by significant increases in dibenzothiophenes. Total PAH fluxes in the modern sediments of our six study lakes, including one site ∼90 km northwest of the major development area, are now ∼2.5–23 times greater than ∼1960 levels. PAH ratios indicate temporal shifts from primarily wood combustion to petrogenic sources that coincide with greater oil sands development. Canadian interim sediment qual- ity guidelines for PAHs have been exceeded since the mid-1980s at the most impacted site. A paleoecological assessment of Daphnia shows that this sentinel zooplankter has not yet been negatively impacted by decades of high atmospheric PAH deposition. Rather, coincident with increases in PAHs, climate-induced shifts in aquatic primary production related to warmer and drier conditions are the primary environmental drivers producing marked daphniid shifts af- ter ∼1960 to 1970. Because of the striking increase in PAHs, elevated primary production, and zooplankton changes, these oil sands lake ecosystems have entered new ecological states completely distinct from those of previous centuries.

Litter production in Pinus banksiana dominated stands in northern Alberta


Author(s): Fyles, J. W.

Year: 1986

Abstract:
Tree and shrub litter production was measured over 2 years in 12 jack pine (Pinusbanksiana Lamb.) and 2 white spruce (Piceaglauca (Moench) Voss) dominated stands located in the Hondo – Slave Lake and Athabasca Oil Sands areas of north central and northeastern Alberta. Annual and daily production rates were calculated for foliage (by species), male cones, and structural material (bark, twigs). Annual litter fall weights were typical of those measured in other boreal regions and were correlated with stand basal area. Seasonal patterns in daily production rates suggested that three classes of control factors were involved in determining litter fall rates within a stand. Random factors, such as weather, and chronic factors, such as insect or disease activity, contolled foliage and structural litter through most of the year. Predictable seasonal factors relating to plant physiological state controlled autumn foliage litter fall in deciduous and most coniferous species and production of male cone litter in early summer.

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