This document compares different techniques for projecting the population of bald eagles using historical count data from 1957 to 1987. It finds that the population projections differ significantly depending on which counting method is used, with the Fawks study predicting a growth rate that differs statistically from current known population sizes. Different growth models are best fits for the data from each counting method. The document cautions that not all population models are literally true, but some can still be useful for management purposes like determining when a species might be delisted.
Presentation at the 5th Global Science Conference on Climate-Smart Agriculture.
Title: Data science to support climate smart agriculture in South Asia: How can crucial data gaps be filled with big data stacks?
Speaker: Timothy J. Krupnik
Poster RDAP13: Provenance of Figures in the Global Change Information SystemASIS&T
Justin Goldstein, Curt Tilmes, Ana Pinheiro Privette, Robert David, Marshall Ma, Jin Zheng, Steven Aulenbach and Fred Burnett
Provenance of Figures in the Global Change Information System
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
Self-organzing maps in Earth Observation Data Cube AnalysisLorena Santos
Earth Observation (EO) Data Cubes infrastructures model
analysis-ready data generated from remote sensing images as multidimensional cubes (space, time and properties), especially for satellite image time series analysis. These infrastructures take advantage of big data technologies and methods to store, process and analyze the big amount of Earth observation satellite images freely available nowadays. Recently, EO Data Cubes infrastructures and satellite image time series analysis
have brought new opportunities and challenges for the Land Use and Cover Change (LUCC) monitoring over large areas. LUCC have caused a great impact on tropical ecosystems, increasing global greenhouse gases emissions and reducing the planet’s biodiversity. This paper presents the
utility of Self-Organizing Maps (SOM) neural network method in the
process to extract LUCC information from EO Data Cubes infrastructures, using image time series analysis. Most classification techniques to create LUCC maps from satellite image time series are based on supervised learning methods. In this context, SOM is used as a method to assess land use and cover samples and to evaluate which spectral bands and vegetation indexes are best suitable for the separability of land use and cover classes. A case study is described in this work and shows the potential of SOM in this application
Presentation at the 5th Global Science Conference on Climate-Smart Agriculture.
Title: Data science to support climate smart agriculture in South Asia: How can crucial data gaps be filled with big data stacks?
Speaker: Timothy J. Krupnik
Poster RDAP13: Provenance of Figures in the Global Change Information SystemASIS&T
Justin Goldstein, Curt Tilmes, Ana Pinheiro Privette, Robert David, Marshall Ma, Jin Zheng, Steven Aulenbach and Fred Burnett
Provenance of Figures in the Global Change Information System
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
Self-organzing maps in Earth Observation Data Cube AnalysisLorena Santos
Earth Observation (EO) Data Cubes infrastructures model
analysis-ready data generated from remote sensing images as multidimensional cubes (space, time and properties), especially for satellite image time series analysis. These infrastructures take advantage of big data technologies and methods to store, process and analyze the big amount of Earth observation satellite images freely available nowadays. Recently, EO Data Cubes infrastructures and satellite image time series analysis
have brought new opportunities and challenges for the Land Use and Cover Change (LUCC) monitoring over large areas. LUCC have caused a great impact on tropical ecosystems, increasing global greenhouse gases emissions and reducing the planet’s biodiversity. This paper presents the
utility of Self-Organizing Maps (SOM) neural network method in the
process to extract LUCC information from EO Data Cubes infrastructures, using image time series analysis. Most classification techniques to create LUCC maps from satellite image time series are based on supervised learning methods. In this context, SOM is used as a method to assess land use and cover samples and to evaluate which spectral bands and vegetation indexes are best suitable for the separability of land use and cover classes. A case study is described in this work and shows the potential of SOM in this application
Student Name SCIN 401 MammalogyCase Study Assignment Wee.docxemelyvalg9
Student Name: SCIN 401 Mammalogy
Case Study Assignment Week 7: Home Range Calculations
Read the following information about home ranges and the case example. Follow the directions for the calculations. Answer the essay question completely using thoughtful ideas, the course text, and outside reference sources. Proofread answers for potential writing errors. Part A. Background on Home Ranges
Intraspecific competition for resources and other behavioral interactions can drive territoriality and establishment of home ranges. Many mammal species exhibit forms of territoriality that result in home ranges.A home range is defined by Burt (1943) as “that area traversed by the individual in its normal activities of food gathering, mating, and caring for young.” Mammals known to exhibit territoriality occur in the orders Perissodactyla, Carnivora, Lagomorpha, Rodentia, Primates, Chiroptera, and Socicomorpha (Vaughan et al. 2011). Study of home ranges can increase knowledge about the species ecology, including habitat quality, and behavior.
Calculation of home ranges from raw location data is beyond the scope of this case study; however, it is valuable to know in general terms how home ranges are calculated. Generally individuals are marked and then location data is gathered by capture-recapture, observation capture, radio-telemetry, and/or satellite data (e.g., collars with GPS transmitters have been used on large species like polar bears [http://alaska.usgs.gov/science/biology/polar_bears/tracking.html]). Location data is graphed usually with the aid of mapping software like ArcGIS (http://www.esri.com/software/arcgis/index.html) or even Google Earth Pro (Taulman, 2010). Statistical techniques are used to calculate and analyze home ranges such as minimum convex polygons, bivariate ellipses, adaptive and fixed kernels, and even a Brownian bridge technique (Mitchell, 2006). Home ranges can be overlayed with other GIS data layers (e.g., habitat cover type, elevation, water sources, etc.), to create powerful analyses and hypotheses for further research.
Reference Sources
Burt, W. H. (1943). Territoriality and home range concepts as applied to mammals. Journal of Mammalogy, 24:346-352
Mitchell, Brian R. 2006. Comparison of programs for fixed kernel home range analysis. Remotely Wild (Issue 21, June 2006).
Taulman, J.F. (2010). Display of Animal Location Data and Kernel Home Range Contours in Google Earth Pro. The American Midland Naturalist 164(1):157-164. 2010 doi: 10.1674/0003-0031-164.1.157
Vaughan , T.A., Ryan, J.M., &Czaplewski, N.J.(2011)Chapter 23, Territoriality and Home Range. Mammalogy(course text), Jones & Bartlett, Sudbury MAPart B. Case Example
(
Photo Credit: Sally King, U.S. National Park Service
)In this case example, three populations of Abert Squirrel (Sciurusaberti) also called the tassel-eared squirrelwere studied. Sciurusaberti is found in ponderosa (Pinuspondersa) forests inWyoming, Colorado, New Mexico, Arizona, Utah,.
In the fall of 2002, at the request of Concerned Citizens for Topeka, a group of researchers from Washburn University began a research project designed to gain some understanding of how people experience discrimination in Topeka, Kansas.
The research team developed a survey instrument and a set of guiding questions that would appropriately glean the data desired. The survey instrument was mailed to every third registered voter on that list. A total of 18,000 surveys were mailed.
In addition to survey data, the research team collected qualitative data at nine town hall meetings over the course of nine weeks (one in each city council district). The qualitative data summarized in this report came from written responses added to the survey instrument and a series of open-ended questions asked by the research team during the town hall meetings.
Washburn University Study of Discrimination in Topeka by
Richard B. Ellis, Ph.D., Associate Professor of Human Services
Michael Birzer, Ph.D, Assistant Professor Criminal Justice
Tiffany Norris-Miller, BAS, Human Services
Renee Kahler, BAS, Human Services
Travis Barnhart, Student, Social Work
Spatial representation of data in Urban Planning and DesignRoberto Rocco
This a lecture of data, statistics and spatial representation and understanding of data. This is important for planners and designers who need to understand social trends in space and how to communicate them to an audience. I typically teach this lecture in 50 minutes (I skip some slides). Feel free to use material here, but do the right thing: acknowledge the source.
Data Journalism lecture - Week 5: Storytelling with Data
Lecture date: 7 Oct 2015
MA in Journalism
National University of Ireland, Galway
Title slide image from The Data Journalism Handbook
TERMS OF DEMOGRAPHIC DATA SOURCES
Demography : study of statistical description and analysis of human population.
Population : summation of all the organism of the same group in a particular geographical area.
Population census : a complete population count at a point in time within a particular area.
Vital registration : registration on live Births, Deaths, Fetal deaths, Marriages, and Divorces.
Sample Survey: representative portion of the population .
DEMOGRAPHIC DATA
Demographic data is the study of the population its static and dynamic aspects.
Static aspect (age, sex, race etc.)
Dynamic aspect (fertility, morality, migration)
Statistics Informed Decisions Using Data 5th edition by Michael Sullivan solu...ssuserf63bd7
https://qidiantiku.com/solution-manual-for-statistics-informed-decisions-using-data-5th-edition-by-michael-sullivan.shtml
Statistics Informed Decisions Using Data 5th edition by Michael Sullivan solution manual.pdf
name：Solution manual for Statistics: Informed Decisions Using Data 5th edition by Michael Sullivan
Edition：5th edition
author：by Michael Sullivan III
ISBN：ISBN-10 : 0134135377
ISBN-13 : 9780134135373
type：solution manual
format：word/zip
All chapter include
For the last few centuries, statistics has remained a part of mathematics as the original
work was done by mathematicians like Pascal (1623-1662), James Bernoulli (1654-1705),
De Moivre (1667-1754), Laplace (1749-1827), Gauss (1777-1855), Lagrange, Bayes,
Markoff, Euler etc. These mathematicians were mainly interested in the development of
the theory of probability as applied to the theory of games and other chance phenomena.
Till early nineteenth century, statistics was mainly concerned with official statistics needed
for the collection of information on revenue, population and area of land under cultivation
etc. of a state or kingdom.
The science of statistics developed gradually and its field of application widened day
by day. Hence, it is difficult to give an exact definition of statistics. The definition changed
from time to time depending upon its use and application. Numerous definitions have
been coined by different people. These definitions reflect the statistical angle and field of
activity.
Student Name SCIN 401 MammalogyCase Study Assignment Wee.docxemelyvalg9
Student Name: SCIN 401 Mammalogy
Case Study Assignment Week 7: Home Range Calculations
Read the following information about home ranges and the case example. Follow the directions for the calculations. Answer the essay question completely using thoughtful ideas, the course text, and outside reference sources. Proofread answers for potential writing errors. Part A. Background on Home Ranges
Intraspecific competition for resources and other behavioral interactions can drive territoriality and establishment of home ranges. Many mammal species exhibit forms of territoriality that result in home ranges.A home range is defined by Burt (1943) as “that area traversed by the individual in its normal activities of food gathering, mating, and caring for young.” Mammals known to exhibit territoriality occur in the orders Perissodactyla, Carnivora, Lagomorpha, Rodentia, Primates, Chiroptera, and Socicomorpha (Vaughan et al. 2011). Study of home ranges can increase knowledge about the species ecology, including habitat quality, and behavior.
Calculation of home ranges from raw location data is beyond the scope of this case study; however, it is valuable to know in general terms how home ranges are calculated. Generally individuals are marked and then location data is gathered by capture-recapture, observation capture, radio-telemetry, and/or satellite data (e.g., collars with GPS transmitters have been used on large species like polar bears [http://alaska.usgs.gov/science/biology/polar_bears/tracking.html]). Location data is graphed usually with the aid of mapping software like ArcGIS (http://www.esri.com/software/arcgis/index.html) or even Google Earth Pro (Taulman, 2010). Statistical techniques are used to calculate and analyze home ranges such as minimum convex polygons, bivariate ellipses, adaptive and fixed kernels, and even a Brownian bridge technique (Mitchell, 2006). Home ranges can be overlayed with other GIS data layers (e.g., habitat cover type, elevation, water sources, etc.), to create powerful analyses and hypotheses for further research.
Reference Sources
Burt, W. H. (1943). Territoriality and home range concepts as applied to mammals. Journal of Mammalogy, 24:346-352
Mitchell, Brian R. 2006. Comparison of programs for fixed kernel home range analysis. Remotely Wild (Issue 21, June 2006).
Taulman, J.F. (2010). Display of Animal Location Data and Kernel Home Range Contours in Google Earth Pro. The American Midland Naturalist 164(1):157-164. 2010 doi: 10.1674/0003-0031-164.1.157
Vaughan , T.A., Ryan, J.M., &Czaplewski, N.J.(2011)Chapter 23, Territoriality and Home Range. Mammalogy(course text), Jones & Bartlett, Sudbury MAPart B. Case Example
(
Photo Credit: Sally King, U.S. National Park Service
)In this case example, three populations of Abert Squirrel (Sciurusaberti) also called the tassel-eared squirrelwere studied. Sciurusaberti is found in ponderosa (Pinuspondersa) forests inWyoming, Colorado, New Mexico, Arizona, Utah,.
In the fall of 2002, at the request of Concerned Citizens for Topeka, a group of researchers from Washburn University began a research project designed to gain some understanding of how people experience discrimination in Topeka, Kansas.
The research team developed a survey instrument and a set of guiding questions that would appropriately glean the data desired. The survey instrument was mailed to every third registered voter on that list. A total of 18,000 surveys were mailed.
In addition to survey data, the research team collected qualitative data at nine town hall meetings over the course of nine weeks (one in each city council district). The qualitative data summarized in this report came from written responses added to the survey instrument and a series of open-ended questions asked by the research team during the town hall meetings.
Washburn University Study of Discrimination in Topeka by
Richard B. Ellis, Ph.D., Associate Professor of Human Services
Michael Birzer, Ph.D, Assistant Professor Criminal Justice
Tiffany Norris-Miller, BAS, Human Services
Renee Kahler, BAS, Human Services
Travis Barnhart, Student, Social Work
Spatial representation of data in Urban Planning and DesignRoberto Rocco
This a lecture of data, statistics and spatial representation and understanding of data. This is important for planners and designers who need to understand social trends in space and how to communicate them to an audience. I typically teach this lecture in 50 minutes (I skip some slides). Feel free to use material here, but do the right thing: acknowledge the source.
Data Journalism lecture - Week 5: Storytelling with Data
Lecture date: 7 Oct 2015
MA in Journalism
National University of Ireland, Galway
Title slide image from The Data Journalism Handbook
TERMS OF DEMOGRAPHIC DATA SOURCES
Demography : study of statistical description and analysis of human population.
Population : summation of all the organism of the same group in a particular geographical area.
Population census : a complete population count at a point in time within a particular area.
Vital registration : registration on live Births, Deaths, Fetal deaths, Marriages, and Divorces.
Sample Survey: representative portion of the population .
DEMOGRAPHIC DATA
Demographic data is the study of the population its static and dynamic aspects.
Static aspect (age, sex, race etc.)
Dynamic aspect (fertility, morality, migration)
Statistics Informed Decisions Using Data 5th edition by Michael Sullivan solu...ssuserf63bd7
https://qidiantiku.com/solution-manual-for-statistics-informed-decisions-using-data-5th-edition-by-michael-sullivan.shtml
Statistics Informed Decisions Using Data 5th edition by Michael Sullivan solution manual.pdf
name：Solution manual for Statistics: Informed Decisions Using Data 5th edition by Michael Sullivan
Edition：5th edition
author：by Michael Sullivan III
ISBN：ISBN-10 : 0134135377
ISBN-13 : 9780134135373
type：solution manual
format：word/zip
All chapter include
For the last few centuries, statistics has remained a part of mathematics as the original
work was done by mathematicians like Pascal (1623-1662), James Bernoulli (1654-1705),
De Moivre (1667-1754), Laplace (1749-1827), Gauss (1777-1855), Lagrange, Bayes,
Markoff, Euler etc. These mathematicians were mainly interested in the development of
the theory of probability as applied to the theory of games and other chance phenomena.
Till early nineteenth century, statistics was mainly concerned with official statistics needed
for the collection of information on revenue, population and area of land under cultivation
etc. of a state or kingdom.
The science of statistics developed gradually and its field of application widened day
by day. Hence, it is difficult to give an exact definition of statistics. The definition changed
from time to time depending upon its use and application. Numerous definitions have
been coined by different people. These definitions reflect the statistical angle and field of
activity.
Artificial Reefs by Kuddle Life Foundation - May 2024punit537210
Situated in Pondicherry, India, Kuddle Life Foundation is a charitable, non-profit and non-governmental organization (NGO) dedicated to improving the living standards of coastal communities and simultaneously placing a strong emphasis on the protection of marine ecosystems.
One of the key areas we work in is Artificial Reefs. This presentation captures our journey so far and our learnings. We hope you get as excited about marine conservation and artificial reefs as we are.
Please visit our website: https://kuddlelife.org
Our Instagram channel:
@kuddlelifefoundation
Our Linkedin Page:
https://www.linkedin.com/company/kuddlelifefoundation/
and write to us if you have any questions:
info@kuddlelife.org
UNDERSTANDING WHAT GREEN WASHING IS!.pdfJulietMogola
Many companies today use green washing to lure the public into thinking they are conserving the environment but in real sense they are doing more harm. There have been such several cases from very big companies here in Kenya and also globally. This ranges from various sectors from manufacturing and goes to consumer products. Educating people on greenwashing will enable people to make better choices based on their analysis and not on what they see on marketing sites.
Natural farming @ Dr. Siddhartha S. Jena.pptxsidjena70
A brief about organic farming/ Natural farming/ Zero budget natural farming/ Subash Palekar Natural farming which keeps us and environment safe and healthy. Next gen Agricultural practices of chemical free farming.
"Understanding the Carbon Cycle: Processes, Human Impacts, and Strategies for...MMariSelvam4
The carbon cycle is a critical component of Earth's environmental system, governing the movement and transformation of carbon through various reservoirs, including the atmosphere, oceans, soil, and living organisms. This complex cycle involves several key processes such as photosynthesis, respiration, decomposition, and carbon sequestration, each contributing to the regulation of carbon levels on the planet.
Human activities, particularly fossil fuel combustion and deforestation, have significantly altered the natural carbon cycle, leading to increased atmospheric carbon dioxide concentrations and driving climate change. Understanding the intricacies of the carbon cycle is essential for assessing the impacts of these changes and developing effective mitigation strategies.
By studying the carbon cycle, scientists can identify carbon sources and sinks, measure carbon fluxes, and predict future trends. This knowledge is crucial for crafting policies aimed at reducing carbon emissions, enhancing carbon storage, and promoting sustainable practices. The carbon cycle's interplay with climate systems, ecosystems, and human activities underscores its importance in maintaining a stable and healthy planet.
In-depth exploration of the carbon cycle reveals the delicate balance required to sustain life and the urgent need to address anthropogenic influences. Through research, education, and policy, we can work towards restoring equilibrium in the carbon cycle and ensuring a sustainable future for generations to come.
WRI’s brand new “Food Service Playbook for Promoting Sustainable Food Choices” gives food service operators the very latest strategies for creating dining environments that empower consumers to choose sustainable, plant-rich dishes. This research builds off our first guide for food service, now with industry experience and insights from nearly 350 academic trials.
2. Data Origin
• Global Population Dynamics Database
▫ Havera and Kruse (1988)
Audubon Society [Ground Counts]
Illinois Natural History Survey [Aerial Counts]
Elton Fawks [Ground Counts & Aerial Counts]
• Current known population data between 2010-2013
▫ ENF (2013)
24 hour ground count
• Mid-winter Bald Eagle Counts
3. Study Area
• Mississippi and Illinois River Floodplain, Illinois
• Counts from 1957-1987
• Slightly different between studies
4. Figure 2. Inventory sites for
bald eagles in Illinois conducted
by the Illinois Natural History
Survey, 1972-1987 (Havera &
Kruse 1988). These sites are
indicated by the black hash
marks, bold circles and bold
squares. Counts conducted by
the Audubon Society generally
occurred near chapter locations
(Havera & Kruse 1988)
indicated by the unfilled circles.
Inventory sites of the Fawks
surveys occurred north of the
bold black line to the northern
border of the state as deducted
by locations given by Havera &
Kruse (1988).
5. Figure 1. Bald eagle population size from 1957 to 1987 from each survey method reported by
Fawks, the Audubon Society, and the INHS as provided by Havera and Kruse (1988).
6. Figure 1. Bald eagle population size from 1957 to 1987 from each survey method reported by
Fawks, the Audubon Society, and the INHS as provided by Havera and Kruse (1988).
7. Thoughts
• “Couldn’t you find the actual population size and
compare each method to that?”-comparison of
method accuracy
• Actual population size is only truly known when the
population is closed and when we can obtain a
census
• We accept the data as truth-philosophical approach
8. Goals 1. Do these methods
produce significantly
different results?
2. What are the growth
rates of the population
and potential future
population size given
the data?
3. And how do these
projections differ from
the current known
population size?
9. Data Analysis
1. Frequentist Approach (SAS)
▫ 8 years of common collection (1973-1980)
▫ Student’s T-test
2. Estimation of growth rate and best fit linear
model and average lambda (Excel)
3. Frequentist Approach (SAS)
▫ Population projections from all 3 surveys compared
against the current known population data
▫ Student’s T-test
10. Results/Conclusions
1. Raw data; P and T values conclude-Ha: Fawks ≠
INHS, Fawks ≠ Audubon, Audubon = INHS
2. Trendlines with maximized correlations; each
survey technique predict a different growth model
3. Growth rate predictions from the Fawks study
differ statistically from current known population
sizes
Why?
11. Figure 3. Bald Eagle population growth trends across the 3 survey types projected into the present where the current
known population data is represented by ENF when 8 years of common data are included.
y = 78.3x + 532.39
R² = 0.5335
y = 41.983x + 285.86
R² = 0.4065
y = 48.783x + 303.86
R² = 0.4382
0
500
1000
1500
2000
2500
3000
3500
4000
4500
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
PopulationSize
Year
Fawks
Audobon
INHS
ENF
Linear (Fawks)
Linear (Audobon)
Linear (INHS)
12. Figure 4. Bald Eagle population growth trends across the 3 survey types projected into the present
where the current known population data is represented by ENF when all years of data are present.
y = 336.94e0.0495x
R² = 0.7551
y = 23.756x0.9689
R² = 0.2917
y = 27.017x - 7.6913
R² = 0.6523
0
1000
2000
3000
4000
5000
6000
PopulationSize
Year
Fawkes
Audobon
INHS
ENF
Expon. (Fawkes)
Power (Audobon)
Linear (INHS)
13. Discussion
• Not all models are true, but some are helpful.
• Philosophical approach to data collection and
interpretation
• Different estimations of abundance and growth
rate can affect the way we manage a species
• This specific example: determine when we can
delist or predicting when recovery will occur
![Data Origin
• Global Population Dynamics Database
▫ Havera and Kruse (1988)
Audubon Society [Ground Counts]
Illinois Natural History Survey [Aerial Counts]
Elton Fawks [Ground Counts & Aerial Counts]
• Current known population data between 2010-2013
▫ ENF (2013)
24 hour ground count
• Mid-winter Bald Eagle Counts](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)