Dear Writer, Please do a PowerPoint presentation on my paper that was completed. I am attaching my paper so can have an idea what to do. Please include speaker notes in the presentation. Using PowerPoint develop a 22-slide presentation that demonstrates your mastery of the Program Outcomes by discussing the results of your Individual Project.  The following slides should be presented: 1)      Title Slide 2)      Overview 3)      Research Problem 4)      Research Intent 5)      Research Question(s)/Hypothesis 6)      Literature Review Key Points 7)      Methodology 8)      Results 9)      Conclusions 10)  Recommendations 11)  Summary Chapter 1 Introduction Preparation and response to natural disasters is a serious logistical challenge. Significant resources are used by intergovernmental, governmental, and non-governmental organizations to prepare and respond to the effects of natural disasters. When a natural disaster occurs, such organizations mobilize resources to respond. Recently, technological advancements in autonomous, semiautonomous, and unmanned vehicles have increased the utility of such vehicles while reducing costs. The increased use of UAVs has created a new dimension to Synthetic Aperture Radar (SAR) operations. In real life, the use of UAVs can be beneficial in cases where rapid decisions are required or the use of manpower is limited (Boehm et al., 2017). Natural disasters have significantly damaged transportation infrastructure including railways and roads. Additionally, barrier lakes and landslides pose a serious threat to property and life in areas affected. When infrastructure is interrupted with heavy rescue equipment, rescue vehicles, suppliers and rescue teams face challenges to reach disaster-hit areas. As a result, efforts to provide humanitarian aid is hampered (Tatsidou et al., 2019). The traditional approaches of responding to natural disasters are unable to meet the requirements to support the process of disaster decision making. UAVs are well equipped to navigate areas affected by natural disasters and provide humanitarian aid. This study aims to explore the viability of using the MQ-8B Fire Scout in providing humanitarian aid in areas affected by natural disasters. The document provides a literature review on the use of UAVs in providing humanitarian aid when natural disasters have occurred. The study compares the viability of using MQ-8B to MH-60 in conducting rescue operations in areas affected by disasters. Significance of the Study This study was conducted to determine the effectiveness of using UAVs in providing humanitarian aid in areas affected by natural disasters. The study assists in developing general knowledge and bridge the existing gap in providing humanitarian aid using UAVs. The findings of this study increase knowledge of the effectiveness of UAVs in responding to natural disasters and provide more insights on useful methods to respond to affected areas. Ultimately, these insights cou.

1. Dear Writer,
Please do a PowerPoint presentation on my paper that was
completed. I am attaching my paper so can have an idea what to
do.
Please include speaker notes in the presentation.
Using PowerPoint develop a 22-slide presentation that
demonstrates your mastery of the Program Outcomes by
discussing the results of your Individual Project. The following
slides should be presented:
1) Title Slide
2) Overview
3) Research Problem
4) Research Intent
5) Research Question(s)/Hypothesis
6) Literature Review Key Points
7) Methodology
8) Results
9) Conclusions
10) Recommendations
11) Summary
Chapter 1
Introduction
Preparation and response to natural disasters is a serious
logistical challenge. Significant resources are used by

2. intergovernmental, governmental, and non-governmental
organizations to prepare and respond to the effects of natural
disasters. When a natural disaster occurs, such organizations
mobilize resources to respond. Recently, technological
advancements in autonomous, semiautonomous, and unmanned
vehicles have increased the utility of such vehicles while
reducing costs. The increased use of UAVs has created a new
dimension to Synthetic Aperture Radar (SAR) operations. In
real life, the use of UAVs can be beneficial in cases where rapid
decisions are required or the use of manpower is limited
(Boehm et al., 2017).
Natural disasters have significantly damaged transportation
infrastructure including railways and roads. Additionally,
barrier lakes and landslides pose a serious threat to property and
life in areas affected. When infrastructure is interrupted with
heavy rescue equipment, rescue vehicles, suppliers and rescue
teams face challenges to reach disaster-hit areas. As a result,
efforts to provide humanitarian aid is hampered (Tatsidou et al.,
2019). The traditional approaches of responding to natural
disasters are unable to meet the requirements to support the
process of disaster decision making. UAVs are well equipped to
navigate areas affected by natural disasters and provide
humanitarian aid.
This study aims to explore the viability of using the MQ-
8B Fire Scout in providing humanitarian aid in areas affected by
natural disasters. The document provides a literature review on
the use of UAVs in providing humanitarian aid when natural
disasters have occurred. The study compares the viability of
using MQ-8B to MH-60 in conducting rescue operations in
areas affected by disasters.
Significance of the Study
This study was conducted to determine the effectiveness of
using UAVs in providing humanitarian aid in areas affected by
natural disasters. The study assists in developing general
knowledge and bridge the existing gap in providing

3. humanitarian aid using UAVs. The findings of this study
increase knowledge of the effectiveness of UAVs in responding
to natural disasters and provide more insights on useful methods
to respond to affected areas. Ultimately, these insights could
help develop more knowledge about the fate of UAVs associated
with rescue operations. The findings of this study can be used
as the basis for future studies by researchers interested in this
topic.
Statement of the Problem
The problem to be addressed in this study is the loss of
human life during natural disasters which could be prevented or
reduced through enhanced delivery of humanitarian aid.
According to Luo et al. (2017), the earthquake that hit Haiti in
2010 claimed approximately 160,000 lives. The 2004 Indian
Ocean tsunami left approximately 360,000 people dead and
more than 1,300,000 others displaced (Luo et al., 2017). While
there were efforts taken to deliver humanitarian aid in both
instances, the use of manned systems proved to be limited to
areas that presented less risk to the rescue teams.
After a natural disaster, governmental and non-
governmental organizations provide significant resources for
rescue and recovery missions. However, the nature of damaged
infrastructure makes it impossible for response vehicles to reach
the affected areas. This demonstrates the inefficiency associated
with traditional methods of providing humanitarian aid in such
situations. As a result, there exists a need for a more robust
approach to providing humanitarian aid after natural disasters to
mitigate the loss of life in the future. The use of UAVs can
augment response teams in providing humanitarian help to
affected areas in a cost-effective and timely manner.
Purpose Statement
The focus of this research was the ability of the Northrop
Grumman MQ-8B Fire Scout to augment humanitarian aid
operations for mitigating loss of life after natural disasters. The

4. research analyzed the mishap rates of the MQ-8B compared to
the MH-60, and looked at how the Fire Scout can be used
mutually for military operations, as well its capacity for
provisioning humanitarian aid. Given the available speed and
ability of the UAV to access high-risk places, the MQ-8B Fire
Scout can offer a solution to the existing problem (Gomez &
Purdie, 2017).
Research Question and Hypothesis
This study aims to answer the following research questions
(RQ):
RQ1: How viable is the deployment of the MQ-8B Fire Scout
for a more expedient and cost-effective solution to delivering
humanitarian aid compared to using the MH-60 Sea Hawk?
RQ2: What are the advantages and disadvantages that could be
associated with the use of the MQ-8B Fire Scout for identifying
victims, water drops for wildfire hotspots, and first aid drops
for survivors post-natural disaster?
The following hypothesis (H) has been formulated for the study:
H0: There is no statistical difference in safety when using the
Northrop Grumman MQ-8B Fire Scout when compared to
manned vehicles to provide humanitarian aid in areas affected
by a disaster.
H1: There is a statistical difference in safety when using the
Northrop Grumman MQ-8B Fire Scout when compared to
manned vehicles to provide humanitarian aid in areas affected
by a disaster.
Delimitations
This study focused only on the potential use of UAVs in rescue
operations to provide humanitarian aid to individuals in areas
affected by natural disasters. As a result, the study does not
provide a description of how the UAVs can be used in
reconnaissance missions conducted by military personnel. The
study does not describe how UAVs can be used to monitor
riparian areas and pollution in marine areas.

5. Limitations and Assumptions
One of the major limitations of the research is the significant
costs associated with the use of UAVs and more so with the
MQ-8B Fire Scout. Various UAVs would need to be purchased
to facilitate this study. However, due to the high costs, the
researchers settled on less efficient UAVs that could not
provide accurate information. The physical demand of the
terrain, variation in weather conditions, and less optimal use of
machine tools are some of the factors that affected the study.
These factors have a significant impact on situational awareness
and affect how data is interpreted from UAVs. The UAVs used
in the study had shorter ranges, and therefore, could not
generate a great deal of information. Another key limitation of
this study is observer bias that may have compromised the
results.
List of Acronyms
AIS Automatic Identification System
C2 Command and Control communication system
CONOPS Concept of Operations
DTM Digital Terrain Model
DOD Department of Defense
E.O. Electro-optical camera
ELT Emergency Locator Transmitter
FAA Federal Aviation Administration
FEBA Forward Edge of the Battle Area
FLIR Forward-Looking Infra-Red
GAO Government Accountability Office
GPS Global Positing Systems
H Hypothesis
IMINT Imagery Intelligence
ISR Intelligence Surveillance Reconnaissance
LCS Littoral Combat ships
NCBI National Center for Biotechnology Information
NTTL Naval Tactical Task List
OSHA Occupational Safety and Health Administration

6. RDML Rear Admiral Lower Half
RQ Research question
SAR Synthetic Aperture Radar
SIGINT Signals Intelligence
SIL System Integration Laboratories
TCDL Tactical Common Data Link
TRB Transportation Research Board
UAVs Unmanned Aerial Vehicles
VTUAV Vertical Take-Off and Landing Tactical Unmanned
Aerial Vehicle
16
2
Chapter II
Review of the Relevant Literature
Providing humanitarian aid for people affected by natural
disasters has become an issue of major concern not only to
governments but also to other non-governmental organizations.
Destruction of existing infrastructure by natural disasters has
increased public interest in the development of effective tools to
provide humanitarian aid to disaster-hit areas. According to
Macias, Angeloudis, and Ochieng 2018, unmanned aerial
vehicles are the logical choice for responding to natural
disasters. A review of relevant research was conducted in this
chapter to determine the underlying knowledge of the
effectiveness of UAVs in providing humanitarian aid. This
review delineates factors that may affect the optimum use of
UAVs in areas affected by natural disasters.
Origins of UAV and its Applications
The first UAV was developed in World War I under the concept
of cruise missiles to attack enemies from short distances. The

7. first UAV was a wooden biplane with a range of 75 miles. This
technology-focused on attacking a specific location with zero
chance of return. However, by the 1950s, the United States Air
Force was able to develop a UAV capable of returning after
attacking a particular point. During World War II, American
soldiers were able to use UAVs to spy on enemies. In the late
1960s, the United States Air Force engineers embarked on
developing UAVs with better electrical systems to observe
activities of the enemies with better precision (Tatsidou et al.,
2019).
The significant technological developments since that time
have led to improved UAVs that can take part in more delicate
and complex missions. The use of advanced electronic
controlling systems, better radio systems, high-resolution
digital cameras, sophisticated computers, and advanced Global
Positing Systems (GPS) allow UAVs to conduct recovery
missions effectively during natural disasters. The quality of
UAVs significantly increased in the 2000s. UAVs are now used
by the military, and by private firms, and by individual owner-
operators. The performance of modern UAVs allows the
platforms to provide humanitarian aid in areas affected by
natural disasters.
Cargo Delivery with UAVs
Multiple studies confirm UAVs are very effective in delivering
items to areas with poor transportation infrastructure. From
delivering important supplies to monitoring damage by the use
of cameras, UAVs can play a significant role in providing
humanitarian aid. When compared to traditional vehicles, UAVs
are more sophisticated due to the improved flexibility and ease
of use. It is more effective and safer to use a UAV to deliver
supplies in dangerous locations than sending a human being.
However, UAVs are unable to carry an excessively heavy load
because of their size and mostly drop cargo while in route
(D'Amato, Notaro & Mattei, 2018).
Unmanned aircraft designers choose to have the UAV

8. release cargo on air or land for a receiver to remove the cargo.
However, for delivering humanitarian aid in disaster-hit areas,
UAVs are designed to drop supplies from the air. Based on the
limited lifting capacity of UAVs, items must be packaged in
small containers (Petrides et al., 2017). Cargo for humanitarian
UAVs normally consists of blood, bandages, syringes, water
purifying tablets, and medicine. Defibrillation attachments may
also be included in the deliverables. These items are light in
nature and can be packaged into small containers to be lifted by
the UAVs. This allows the UAV to travel for long distances
without losing efficiency.
Impact of Weather
The impact of weather on a UAV depends on the power,
equipment, configuration, and size, as well as the exposure time
and the severity of the weather, encountered. Most UAVs have
characteristics and configurations which make the aircraft more
vulnerable to extreme weather conditions compared to manned
aircraft. In general, today's UAVs are more fragile, lighter, and
slower, as well as more sensitive to weather conditions when
compared to manned aircraft. Small UAVs are very susceptible
to extreme weather conditions. Similar to manned aircraft,
certain weather conditions can also affect larger UAVs making
the aircraft difficult to control. (Ranquist & Steiner & Argrow,
2017).
Extreme weather conditions such as snow, humidity,
temperature extremes, solar storms, rain, turbulence, and wind
may diminish the aerodynamic performance of UAVs, cause loss
of communication, and control. These same conditions can also
negatively affect the operator. Most flight regulations currently
in use do not address most of the weather hazards facing UAVs.
Some of the current restrictions pertaining to weather include
remaining 2000 feet away from the ceiling and 500 feet below
clouds, operating under the unaided visual line, and maintaining

9. visibility for 4.83km (Macias, Angeloudis & Ochieng, 2018).
While this eliminates issues of poor visibility, it does not help
to reduce safety hazards associated with clear skies. Clear sky
hazards may include turbulence, glare, and wind.
Glare occurs in clear skies and may affect visibility in
various ways. First, it hinders the direct observation of the
UAV. On a sunny day, it may also be difficult to spot a UAV in
the sky. As a result, operators must use sunglasses in order to
carry out the missions effectively. Second, the operation of
UAVs requires a user interface to be displayed on a tablet,
phone, monitor or any other screen to allow the operator to
track the UAV, change control derivatives, or send commands
while receiving telemetry updates. The sun can overpower the
LCD brightness of the screen, which makes it difficult for the
operator to send the correct information to control the UAV.
Turbulence can also affect the stability of UAVs. Multiple
studies show wind accounted for more than 50% of manned
aircrafts accidents. This percentage is higher for small aircraft.
This demonstrates the impact turbulence may have on small-
unmanned vehicles. The primary ways wind affects UAVs
include reducing endurance, limiting control, and changing
flight trajectory. Strong winds affect the path of a UAV. Wind
speeds may also surpass the maximum speed of UAVs causing
the UAV to struggle in such environments. The impact of
turbulence can make it difficult for the UAVs to deliver
humanitarian aid to affected areas in a timely manner.
Turbulence, wind gusts, and wind shear all have the
potential of affecting control of UAVs and affect an operator’s
ability to complete the mission in the most effective and
expedient manner. UAV control is the ability to maneuver the
UAV by use of roll, pitch, and yaw. Pitch changes the attack
angle for the UAV, roll rotates the UAV, and yaw changes the
direction of the UAV. When the speed of the wind increases
suddenly, it affects the yaw of the UAV making it difficult for
the operator to control it effectively. A horizontal gust can also
roll the UAV and is most dangerous when flying in areas with

10. obstructions.
Operational Flexibility of UAVs
UAVs have increased persistence in air operations compared to
manned systems making the MQ-8B ideal for conducting
humanitarian aid operations. While there are theoretical and
practical limits, utilizing few vehicles allows for continuous
surveillance for a long period of time. The flexibility of the
MQ-8B allows it and other UAVs to carry out operations when
and where other manned aircraft are unable to operate. The
long-endurance capabilities of UAVs allow the air vehicle to
deliver humanitarian aid many hours into a flight, which could
otherwise be impossible with traditional approaches. As a
result, people in areas experiencing natural disasters may
receive supplies continuously.
While both unmanned and manned air operations can be
coordinated by multiple people, not having a physical operator
in the vehicle allows multiple operators to share direct controls.
The user with the immediate need or situational awareness may
assume full control of the UAV. This capability significantly
reduces the timelines of coordination between the UAV and
ground users. With the dire need associated with response
missions, UAVs are better suited to provide humanitarian aid
when compared to the traditional methods, which normally
takes a significant amount of time to reach those affected.
UAV legislation and regulation Environment
The ability to use UAVs for disaster response in the United
States is largely limited by the Federal Aviation Administration
(FAA). The current FAA policy for operating unmanned aerial
vehicles in the United States requires a specific authority to
operate one. In general, any use of UAV requires an
airworthiness certification. However, potential users of UAVs
face significant regulatory challenges in the United States. The
law requires UAVs to include registration numbers in their
markings. Operation circular 91-57 describes the differences

11. between non-hobby use and hobby use of UAVs and operating
restrictions. The FAA has implemented various orders to restrict
the operation of UAVs.
Local governments have developed legislation that describes the
potential use of UAVs in emergency situations. Various
municipalities including Syracuse, New York, and
Charlottesville, Virginia, have implemented further restrictions
such as city purchases of UAVs. Serious concerns about data
collection and privacy have erupted in the United States. The
FAA developed a restriction for privacy in areas of UAVs
operations. Until the private use regulation and legislation
issues surrounding the adoption of UAVs are resolved, it will be
difficult to use them in first response situations. While these
challenges exist, researchers need to explore methods in which
UAVs can be used to provide humanitarian aid during natural
disasters.
Human Factors
In most cases, designers develop controls that work very well in
labs but fail in a real-world situation. The expectation is,
through training and familiarization, humans will be able to
learn and adapt to the controls and displays. However, this
approach is deemed to fail if used in the development of a
human-machine interface. As the capabilities of UAVs increase
every day, the vehicle complexity is also increased. The need to
use automation and advanced technology has also increased.
While these systems are unmanned, it is important to keep in
mind humans are involved in the control and operation of UAVs
(Hildmann & Kovacs, 2019).
The lack of standardization across different UAV human-
machine interfaces results in increased time of training for one
system and increased difficulty in transition to other systems.
Poor optimization of information results in the difficulty of
interpreting system information needed for situational
awareness that supports decision making in stressful situations.
Lack of adaptability and flexibility in UAVs often lead to poor

12. displays and ultimately to poor situational awareness. Lack of
basic sensory cues makes it even more difficult to use UAVs in
response missions. The cues which are relevant in manned
aircraft suddenly become irrelevant in UAVs (Estrada &
Ndoma, 2019). These cues are currently missing in UAVs and
need to be incorporated for increased efficiency.
The development of UAVs that consider the end-user could
increase the effectiveness in responding to natural disasters.
This implies designing human-machine interfaces that are
intuitive, functional, and user-friendly that allow easy
extraction of relevant information by operators. With the
current technological advancements, it is possible to design
intuitive and functional interfaces that utilize the available cues
to maintain high levels of situational awareness needed for
effective, efficient, and safe control of UAVs. This allows
operators to understand various aspects of UAVs and enables
deployment in dangerous areas such as locations affected by
natural disasters.
Sensing and Processing
The success of providing humanitarian aid to areas affected by
natural disasters requires the equipment to have the appropriate
sensors, and to be at the right place, at the right time. This is
important particularly in response situations where emergency
signals, remoteness, weather, and terrain differ significantly.
Even if the UAV is at the right place at the right time, it will be
rendered ineffective without the right sensors. The initial phase
of a rescue mission is the most critical and requires UAVs to
have appropriate sensors. A single UAV may use various
sensors that allow it to come up with a general picture of the
situation (Grogan, Pellerin & Gamache, 2018).
Since the strength of signals is inversely proportional to the
square of the distance, unmanned aerial vehicles designed to
provide humanitarian aid in areas experiencing natural disasters
need to have stronger signals than ground station receivers and
satellites. The signal can be triangulated by multiple UAVs if

13. sent in a digital format. In cases where Emergency Locator
Transmitter (ELT) are not transmitting or activated, infrared
sensors can be used to search the location of the UAV.
Fortunately, sensors in the infrared and low light wavelength
have significantly decreased physical dimensions and costs.
Onboard automation will be very important for effective UAV
operations in extreme conditions.
Mobile Wireless Access Networks
Compared to traditional static sensors, UAVs are still more
costly. Considering the infrastructure needed to respond to such
cases is currently being met by the existing infrastructure, it is
justified that most studies focus on the immediate aftermath of a
natural disaster. UAVs can be used to develop a communication
center to provide victims in an affected area with wireless
communication. Additionally, UAVs can allow people trapped
in areas affected by natural calamities to communicate with the
emergency control center for rescue (Grogan, Pellerin &
Gamache, 2018). One of the benefits of such a system is it
serves those only in the affected location, and this can
maximize performance.
Safety of UAVs
The use of UAVs in rescue operations depends on its ability to
safely operate in the shared aviation environment. As a result,
the UAVs must demonstrate the ability to ensure safety both for
people on the ground and other aircraft. However, there are
various safety risks associated with UAVs which are different
from those presented by manned vehicles. The risk of pilots
losing their lives in flight is reduced because UAVs do not have
occupants. The use of manned vehicles, on the other hand,
implies people will need to use vehicles for movement to areas
affected by natural disasters. As a result, the lives of the rescue
teams are at risk (Estrada & Ndoma, 2019).
UAV designers are aware of the safety concerns associated
with these systems, and more so concerning the poor reliability

14. of such systems in extreme conditions. The designers
understand political support and public trust would fade away in
case of an accident. For this reason, safety remains a top
priority for the UAV community. UAVs have the potential to
provide considerable safety benefits in disaster response
operations. Significant technological developments have the
potential to improve safety associated with UAVs. Advances in
monitoring systems, data exchange networks, communication,
sensor detection systems, and automation will have positive
impacts on UAVs and the UAV community. Automated takeoff
eliminates the possibility of accidents for operators (Escribano
Macias, Angeloudis & Ochieng, 2018).
UAVs use the same airspace as other aircraft. As a result, there
are high chances of collision in the airspace. Numerous studies
by research institutions, universities, industry, and governments
across the world have focused on how collisions can be avoided
in the airspace. While avoiding collisions is a difficult task, the
UAV community has developed to see and avoid capabilities
that allow the operators to avoid obstructions. The distance of
25 feet for detecting obstructions has been clearly provided by
the FAA regulations. The FAA calls for operators to maintain
vigilance to detect and avoid collisions with obstructions while
flying UAVs.
Aviation Aerospace Safety systems and Unmanned Aerospace
systems
The use of unmanned aerospace systems (UAS) has increased
significantly over the past few years. This has raised significant
safety issues concerning UAS. Different countries have
developed policies to govern the operation of UAS in the
aviation aerospace to enhance safety and security. Various
safety initiatives have been developed, most notably the
Commercial Aviation Safety Team (CAST) and the European
Strategic Safety Initiative (ESSI). The purpose of CAST is to
reduce the fatality rate associated with commercial aviation by
80%. The ESSI aims to enhance safety for European citizens

15. through safety analysis and coordination with other global
safety initiatives.
Summary
The review of the literature indicates current research studies
explore the effectiveness of using UAVs in conducting
reconnaissance missions. However, there is a gap in research
focused on the effectiveness of using UAVs to provide
humanitarian aid during and after natural disasters. There is
limited research comparing the effectiveness of using UAVs to
conduct rescue and recovery missions compared to the use of
manned vehicles. There is also limited research focused on
determining the costs and benefits of utilizing emergency
response vehicles and UAVs in responding to natural disasters.
This study determines the resourcefulness of using UAVs
in responding to natural disasters while simultaneously
providing economic benefits. The study increases understanding
to bridge the existing gap in providing humanitarian aid using
UAVs. The findings of this study increase knowledge on the
effectiveness of UAVs in responding to natural disasters and
provide more insights that can be used to respond to affected
areas. Ultimately, these insights could help develop more
knowledge regarding the fate associated with rescue operations.
The findings of this study can be used as the basis for future
studies by researchers interested in this topic.Chapter III
Methodology
Research Approach
The purpose of this quantitative study was to explore the
effectiveness of the MQ-8B and MH-60. A systematic review of
extant literature was conducted. This systematic and progressive
survey is comprised of underlying structured analyses and
methodologies that investigate and report topic-specific studies
regarding objectivity, replicability, transparency, and the
comprehensiveness of the research. The emergence of UAVs has

16. posed critical challenges. Control and monitoring of UAVs
require the increased autonomy of fleets and a reduction of
workload for operators (Cassingham, 2016). The establishment
of the relevant mission model for the MQ-8B is therefore
significant, not only for planning and specification, but also for
control and monitoring. The relevant mission model provides
leverage for mission and fleet states, thereby offering the
operator the necessary information on the mission. This section
undertakes the proposed methodology of the study aiming to
explore the viability of using the MQ-8B Fire Scout in
providing humanitarian aid in areas affected by natural
disasters. The study also compared the practicality of using
MQ-8B to MH-60 in conducting rescue operations in areas
affected by disasters.
Apparatus and materials
Microsoft Excel was used to organize the data of different
UAVs for conducting various humanitarian aid operations. SPSS
software was used SPSS to conduct a descriptive analysis.
Quantitative research methods were applied in the assessment of
the existing data on the usability of UAVs in disaster bound
areas. A mathematical model was used to establish and replicate
mishaps which might occur during humanitarian aid operations
using MQ-8B Fire Scout and the MH-60 Seahawk. The method
of casting leveraged the objective data and existing evidence
concerning the prospects of employing UAVs in disaster-
stricken areas (Gomez & Purdie, 2017). However, based on the
expanse of this research and its realities, a quantitative method
may not be sufficient to adequately justify the hypothesis due to
the subjectivity and inability to address the research themes
outlined in chapter one.
In the wake of a disaster, from dangerous hurricanes,
earthquakes, cyclone storms, wildfires, and delivering toilet
paper to prevent the spread of viruses like COVID-19 (Canales,
2020), the models of undertaking humanitarian aid operations
using UAVs must now address the entire purview of objectivity

17. and subjectivity. For this reason, the study adopted quantitative
methods for conducting data collection and forecasting. The
MQ-8B Fire Scout and the MH-60 Seahawk have been utilized
by disaster relief organizations in the United States and beyond
for more than 15 years. However, the niche in the disaster
response environment, the utility, ethical considerations,
standardization and the legal challenges of the application of
UAVs has remained vastly unexplored.
The quantitative method illustrates or outlines how UAVs have
been utilized by various teams of responders to assess damages
during disasters such as Hurricanes Harvey and Irma in 2017. …
American Journal of Botany 95(1): 66–76. 2008.
66
Movement of seeds from their collection site to other
environ-
ments within a species range for reforestation or restoration
may
increase the risk of maladaptation ( Campbell, 1979 ). Reduced
growth or mortality resulting from maladaptation could reduce
the success of restoration projects, and gene fl ow from
maladapted
planted trees into adjacent native populations could negatively
affect adaptation to local conditions ( McKay et al., 2005 ).
How-
ever, the planting of individuals adapted to new environmental
conditions, e.g., a warmer climate, could be a method to
facilitate
migration and provide a source of genotypes well adapted to
local
populations. Seed transfer should be guided by natural levels of
genetic variation and local adaptation in quantitative traits spe-

18. cifi c to the species in question ( Morgenstern, 1996 ; Hufford
and
Mazer, 2003 ; McKay et al., 2005 ). Understanding genetic
struc-
ture is also necessary for managing breeding programs, evaluat-
ing conservation of genetic resources, and predicting the
possible
effects of climate change ( St. Clair et al., 2005 ).
The ranges of many trees species are predicted to shift higher
in
latitude and elevation as a result of climate change ( Davis and
Shaw, 2001 ; Hamann and Wang, 2006 ). However, at a local
scale,
projected vegetation responses include a combination of eleva-
tional, aspect, and microsite adjustments because the location of
suitable conditions for each taxon shifts within a region (
Bartlein
et al., 1997 ). The potential impacts of predicted warming
under-
score the importance of understanding genetic structure and
adap-
tation of populations to their local environment. For species
threatened by pests and diseases in addition to climate change,
minimizing maladaptation may mean the difference between es-
tablishing or maintaining viable populations and local
extirpation.
Whitebark pine ( Pinus albicaulis Engelm., Pinaceae) is a
high elevation, fi ve-needle pine, and the only North American
member of the stone pines ( Pinus subsection Cembrae ) (
Arno
and Hoff, 1989 ; Price et al., 1998 ; but see Gernandt et al.,
2005 ).
Although of little commercial value, it has tremendous ecologi-

19. cal value and is considered a keystone species ( Tomback et al.,
2001 ). The large, wingless seeds of whitebark pine are an im-
portant food source for the Clark ’ s nutcracker ( Nucifragia co-
lumbiana Wilson), which is its primary dispersal agent and
mutualist ( Tomback, 1978 ; Hutchins and Lanner, 1982 ;
Lanner,
1982 ; Tomback, 1982 ). However, whitebark pine is in decline
throughout most of its range from a synergism of natural
and human-driven causes. Outbreaks of mountain pine beetle
( Dendroctonus ponderosae Hopkins) and decades of fi re sup-
pression have led to mortality and successional replacement by
shade-tolerant species. However, the greatest agent driving the
current decline is the introduced disease white pine blister rust
(caused by the fungus Cronartium ribicola J. C. Fisch. ex
Rabh.). Scientists agree that whitebark pine ecosystems require
immediate restoration to reduce the effects of fi re exclusion
and
blister rust ( McCool and Freimund, 2001 ). Silvicultural tech-
niques can be used to encourage natural regeneration, but in
stands with a compromised seed source or those that need to be
regenerated quickly, planting seedlings (if available) is the sug-
gested restoration practice ( Hoff et al., 2001 ), using blister-
rust-
resistant seedlings when they are available. There is a
widespread
need for restoration and often a limited supply of seed for
white-
bark pine, thus geographic guidelines on seed transfer are
needed for restoration and conservation of this species.
1 Manuscript received 21 September 2006; revision accepted
8 November
2007.
The authors thank the USDA Forest Service regions one, fi ve,
and six;

20. the British Columbia Ministry of Forests; E. C. Manning and
Tweedsmuir
Provincial Parks of British Columbia; and B. Brett of Snowline
Ecological
Consulting, Whistler, B.C. for seed. Many people provided
assistance to
this project, including D. Kolotelo, J. Tuytel, C. Chourmouzis,
D. Watson,
K. Keir, M. Harrison, D. Szohner, P. Smets, J. Krakowski, S,
Trehearne,
and all of the members of the Aitken laboratory at UBC.
Climate data were
provided by Drs. T. Wang and G. Rehfeldt. Funding for this
study came
from the British Columbia Forestry Investment Account through
the Forest
Genetics Council of B.C. to the Centre for Forest Conservation
Genetics
at UBC. Thank you to Drs. A. Yanchuk, M. Whitlock, J.
Whitton, Y. El-
Kassaby, S. Graham, D. Tomback, B. St. Clair, and an
anonymous reviewer
for their helpful comments on earlier drafts of this manuscript.
2 Author for correspondence (e-mail: [email protected])
ECOLOGICAL GENETICS AND SEED TRANSFER
GUIDELINES FOR
PINUS ALBICAULIS (PINACEAE) 1
ANDREW D. BOWER 2 AND SALLY N. AITKEN
Centre for Forest Conservation Genetics, Department of Forest
Sciences, University of British Columbia, 3401-2424 Main
Mall,
Vancouver, British Columbia V6T 1Z4 Canada

21. Whitebark pine ( Pinus albicaulis Engelm.) has greatly
declined throughout its range as a result of introduced disease,
fi re sup-
pression, and other factors, and climate change is predicted to
accelerate this decline. Restoration is needed; however, no
informa-
tion regarding the degree of local adaptation is available to
guide these efforts. A seedling common-garden experiment was
employed to assess genetic diversity and geographic
differentiation ( Q ST ) of whitebark pine for traits involved in
growth and ad-
aptation to cold and to determine climatic variables revealing
local adaptation. Seedlings from 48 populations were grown for
two
years and measured for height increment, biomass, root to shoot
ratio, date of needle fl ush, fall and spring cold injury, and
survival.
Signifi cant variation was observed among populations for most
traits. The Q ST was low (0.07 – 0.14) for growth traits and
moderate
(0.36 – 0.47) for cold adaptation related traits, but varied by
region. Cold adaptation traits were strongly correlated with
mean
temperature of the coldest month of population origins, while
growth traits were generally correlated with growing season
length.
We recommend that seed transfer for restoration favor seed
movement from milder to colder climates to a maximum of 1.9 °
C in
mean annual temperature in the northern portion of the species
range, and 1.0 ° C in the U. S. Rocky Mountains to avoid
maladapta-
tion to current conditions yet facilitate adaptation to future
climates.

22. Key words: genetic variation; geographic differentiation;
local adaptation; Pinus albicaulis ; quantitative traits; seed
transfer;
whitebark pine; white pine blister rust.
67January 2008] BOWER AND AITKEN — ECOLOGICAL
GENETICS OF WHITEBARK PINE
eight replications had ambient soil temperature (ambient
treatment) and the re-
maining four replications (cold treatment) had cooled water
pumped through
hoses buried approximately 25 cm below the surface, which
kept soil tempera-
ture consistently ~8 ° C cooler during the warmest part of the
day. Populations
that were represented in fewer than half of the replications (i.e.,
< 4 in the ambi-
ent or < 2 in the cold treatment) because of mortality were
excluded from the
analysis. The fi nal data set included 40 populations in the
ambient treatment
and 37 in the cold treatment, with 33 populations common to
both treatments
( Table 2 ). The AlphaPlus program ( Mann, 1996 ) was used to
design the plant-
ing layout and assign seedlings randomly within replications.
Seedlings were
planted at 9.5 × 10 cm spacing, with one row of buffer trees
surrounding each
raised nursery bed for which data were not collected. They were
kept well wa-
tered and were fertilized and weeded as needed to provide
conditions optimal

23. for growth for most temperate conifers. Timing of initiation of
growth in the
spring was observed for 2003 and 2004, and at the end of the
2004 growing
season, survival, height growth, aboveground and belowground
oven-dry bio-
mass of seedlings were measured on all replications. Artifi cial
freeze testing
was performed on 5-mm needle segments from all seedlings in
the ambient
treatment in three replications in the fall of 2003 and four
replications in the
spring of 2004. The electrolyte leakage method was used to
quantify cold in-
jury. Details of cold hardiness testing are given in Bower and
Aitken (2006) .
The fi nal data set contained 10 quantitative variables; data
from all trees in
both temperature treatments were available for third-year height
increment,
root biomass, shoot biomass, total biomass, root to shoot ratio,
date of needle
fl ush in 2003 and 2004 ( Table 3 ). Measurements of fall and
spring cold injury
were available from the ambient treatment only. In addition, the
percentage
survival in each soil temperature treatment was tested for
treatment effects.
Data analysis — SAS version 8 ( SAS Institute, 1999 ) was
used for all statisti-
cal analyses. Preliminary analysis showed an increase in
variability of residuals
with an increase in predicted values, so a natural-log
transformation was ap-

24. plied to height increment, root, shoot, and total biomass, and
root to shoot ratio
for all analyses, which helped to equalize variance. For testing
for differences
between soil temperature treatments and genotype-by-
environment interactions
in the quantitative traits, PROC MIXED was used with the
following model for
populations included in both treatments:
y ijklmn = µ + t i + r ( t ) ij + b ( rt ) ijk + p l +
pt il + pr ( t ) ijl + f ( p ) l m + e ijklmn , (Eq. 1)
where y ijklmn is the observed value for tree n in family m
w ithin population l
in incomplete block k in rep j in soil temperature i , µ is
the overall mean, t i is
the effect of temperature i, r ( t ) ij is the effect of rep j
nested within temperature
i , b ( rt ) ijk is the effect of incomplete block k nested
within rep j within tem-
perature i, p l is the effect of population l , pt il is the
interaction of temperature
i and population l , pr ( t ) ijl is the interaction of
population l and rep j within
temperature i , f ( p ) lm is the effect of family m nested
within population l , and
e ijlkmn Temperature, population, and population-by-
temperature interaction
were considered fi xed, while all other effects were considered
random. The
same model was used to analyze each geographic region
separately. Population
Genetic variation and population differentiation have been
assessed in whitebark pine using molecular markers and mono-

25. terpenes ( Yandell, 1992 ; Jorgensen and Hamrick, 1997 ;
Brued-
erle et al., 1998 ; Stuart-Smith, 1998 ; Rogers et al., 1999 ;
Krakowski et al., 2003 ), and results have indicated average to
above average expected heterozygosity compared to other pines
(average H e of 0.16 for whitebark pine vs. 0.13 – 0.16 for
pines
in general [Ledig, 1998]). Population differentiation in white-
bark pine was reported to be low to moderate in all studies ( F
ST
or G ST < 0.09) ( Table 1 ), with signifi cant evidence of
inbreed-
ing ( F is signifi cantly greater than zero). Populations in the
northern (western British Columbia), eastern (Rocky Moun-
tains), and southern regions of the species range (California and
Oregon) are differentiated for monoterpenes ( Zavarin et al.,
1991 ), isozymes ( Yandell, 1992 ) and organelle DNA (
Richard-
son et al., 2002b ). However, levels of genetic variation and
population differentiation in phenotypic traits potentially in-
volved in local adaptation in whitebark pine have not previ-
ously been determined.
In this study, we analyze geographic variation and genetic
differentiation in phenotypic seedling traits in a common-gar-
den experiment in whitebark pine and evaluate degree of local
adaptation to climate for the purpose of developing seed trans-
fer recommendations and predicting the ability of whitebark
pine to adapt to climate change.
MATERIALS AND METHODS
Sample materials — Open-pollinated seeds from 48
populations of white-
bark pine from across most of the species range ( Table 2, Fig.
1 ) were germi-

26. nated in 2002 following seed stratifi cation using the protocol
described by Burr
et al. (2001) . Germinants were sown into individual 10 in 3
(164 cm 3 ) Ray Leach
Cone-tainer super cells (Stuewe and Sons, Corvallis, Oregon,
USA) for their
fi rst growing season. In November 2002, 10-mo-old seedlings
were trans-
planted into a raised nursery bed common garden in Vancouver,
British Colum-
bia (49 ° 13 ’ N, 123 ° 6 ’ W) and grown for two growing
seasons. Seedlings were
planted in an incomplete block alpha design ( Patterson and
Williams, 1976 )
with 12 replications, and 10 four-tree by four-tree incomplete
blocks within
replications. Each replication contained 160 test trees, with
populations repre-
sented by 1 – 18 families (mean 7.9, SE 0.37), with each family
usually repre-
sented once per replication. Because temperatures in Vancouver
are higher than
those in the native environment, two temperature treatments
were imposed:
TABLE 1. Reported values of genetic differentiation for
whitebark pine ( Pinus albicaulis) and other stone pine ( Pinus
subsection Cembrae ) species.
Populations Species Area F ST or G ST Reference
14 P. albicaulis BC, ID, MT, OR 0.075 A. Bower unpublished
manuscript
30 P. albicaulis USA rangewide and northern AB 0.034
Jorgensen and Hamrick 1997
14 P. albicaulis USA Great Basin 0.088 Yandell 1992

27. 29 P. albicaulis Canadian Rockies 0.062 Stuart-Smith 1998
17 P. albicaulis British Columbia 0.061 Krakowski et al. 2003
18 P. albicaulis Rangewide 0.046 a Richardson et al. 2002b
8 P. sibirica Russia ~0.042 Goncharenko et al. 1993b
11 P. sibirica Russia 0.025 Krutovskii et al. 1995
P. koraiensis Coastal Russia 0.016 Potenko and Velikov 2001
19 P. koraiensis Russia 0.015 Potenko and Velikov 1998
3 P. koraiensis Russian far east 0.040 Krutovskii et al. 1995
5 P. pumila Russia 0.043 Goncharenko et al. 1993a
3 P. pumila Kamchatka penn., Russia 0.021 Krutovskii et al.
1995
18 P. pumila Japan 0.170 Tani et al. 1996
5 P. cembra Alps and eastern Carpathians, Ukraine 0.040
Belokon et al. 2005
Notes: AB = Alberta, BC = British Columbia, Canada; ID =
Idaho, MT = Montana, USA.
a Φ ST from cpDNA microsatellite data
68 AMERICAN JOURNAL OF BOTANY [Vol. 95
genetic variance. In this study the variance component for
population ( σ 2 p ) was
used as the among-population variance, and three times the
variance compo-
nent for family within-population (3 σ 2 f ( p ) ) was used
as the within-population
variance. The within-population genetic variation was
approximated as three
times the family variance instead of four as is used for true
half-sibs, because
open-pollinated progeny of whitebark pine are more closely

28. related than half-
sibs due to moderate inbreeding and correlated paternity (
Squillace, 1974 ; Kra-
kowski et al., 2003 ; Bower and Aitken, 2007 ). Values of Q
ST were compared to
all published estimates for whitebark pine for genetic markers (
F ST or G ST ).
Climatic variables used in the analyses were mean annual
tempera-
ture, mean temperature of the coldest month, mean temperature
of the
warmest month, mean annual precipitation, mean summer
precipitation, annual
heat : moisture index, summer heat : moisture index, and
frost-free period. Cli-
matic variables for populations north of 48 ° N were obtained
from PRISM cli-
matic data corrected for local elevation using the Climate BC
model described
by Wang et al. (2006a) . For populations south of 48 ° N,
climatic data were
means were used to test for differences between treatments for
survival percent-
age using the above model with only the temperature and
population effects,
and their interaction.
To test differences among populations within each soil
temperature, PROC
MIXED was used with the REML variance component estimator
and the fol-
lowing model:
y ijklm = µ + r i + b ( r ) ij + p k + rp ik + f ( p )

29. kl + e ijklm , (Eq. 2)
where terms for each effect are the same as in Eq. 1 without the
effect of soil
temperature. All terms were considered random except for
population, which
was fi xed. To obtain estimates of variance components, the
analysis was re-
peated with all effects considered random.
Genetic differentiation among populations was estimated for all
quantitative
traits by calculation of Q ST ( Spitze, 1993 ): Q ST = σ 2
b / ( σ 2 b + 2 σ 2 w ), where σ 2 b is
the among-population variance and σ 2 w is the within-
population additive
TABLE 2. Pinus albicaulis populations, number of seedlings
tested, geographic and climatic data.
Site no. Region Name
No. trees
Lat. o N Long. o W Elev. (m) MAT ( ° C) MTWM ( ° C)
MTCM ( ° C) FFP (d) SH:MAmbient Cold
1 N Serb Creek 5 10 54.71 127.57 1385 0.7 11.4 − 10.7 52 32.7
2 N Hunters Basin 13 29 54.53 127.18 1446 0.3 11.1 − 11 48
34.8
3 N Morice Lake 2 — 54.04 127.48 1231 0.6 11.3 − 11.1 31
44.9
4 N Kimsquit river 1 — 53.19 127.18 900 3 12.5 − 7.2 83 32.9
5 N Heckman Pass 6 — 52.52 125.82 1526 0 9.9 − 10.6 58
46.8
6 N Perkins Peak 3 1 51.83 125.05 1916 − 1.8 7.9 − 11.5 35

30. 31.9
7 N Jesamond 42 19 51.27 121.87 1846 0.9 11.4 − 9.1 45 45
8 N Lime Mtn. 19 13 51.10 121.67 1900 0.5 11.1 − 9.4 39 52.9
9 N Darcy 46 9 50.53 122.58 1800 0.5 11.7 − 9.8 46.1 45.3
10 N Blackcomb 57 40 50.10 122.90 1908 0.6 10 − 7.5 43.7
22.4
11 N Thynne Mtn. 20 1 49.71 120.92 1785 1.9 12.7 − 7.7 48.8
27.4
12 N Manning Park 93 40 49.10 120.67 2000 0.3 10.8 − 8.6
43.8 48.1
13 N Baldy 14 — 49.17 119.25 2154 1.2 12.1 − 8.6 39.6 37.5
14 R Copper Butte 11 9 48.70 118.46 2185 − 0.5 10.4 − 10.2
48.2 30.7
15 R Colville 6 4 48.66 118.46 2154 − 0.1 10.7 − 10.1 49 32.5
16 R Snow Peak — 7 48.58 118.48 2185 0.5 11.2 − 9.7 48.7
35.6
17 R Salmo Mtn. 13 11 48.97 117.10 2092 0 10.7 − 9.3 61.2
21.2
18 R Hooknose Mtn. — 2 48.94 117.43 2215 0.5 11.5 − 8.9 54
28.8
19 R Farnham Ridge 31 26 48.84 116.51 1846 1.5 12.4 − 8.3
70.6 35.4
20 R North Baldy — 7 48.55 117.16 1877 2.6 13.9 − 7 89 41.6
21 R Lunch Peak 37 14 48.38 116.19 1846 2.1 12.4 − 6.6 84
23.9
22 R Our Lake 6 13 47.84 112.81 2277 0.2 11.4 − 9.6 31.3 35.1
23 R Sheep Shed 22 19 47.52 112.80 2154 1 12.3 − 8.9 33.4
43.5
24 R Granite Butte 19 8 46.87 112.47 2338 0.5 12 − 9.1 32.8
47.1
25 R Blacklead Mtn. 23 41 46.64 114.86 2062 1.2 12.3 − 8.1
32.1 26.2
26 R Gospel Peak 22 16 45.63 115.95 2154 1 11.9 − 8.3 26.4
33.9
27 R Heavens Gate 10 3 45.38 116.51 2154 1.2 12.3 − 8.3 40.6
49.2

31. 28 R Mudd Ridge 23 15 45.90 113.45 2400 0.2 11.8 − 9.5 17.5
29
29 R Quartz Hill 27 44 45.71 112.93 2646 − 0.8 10.9 − 10.2
15.8 37
30 R Little Bear 22 26 45.40 111.28 2154 2.1 14.4 − 8.9 40.5
43.4
31 R Picket Pin 4 1 45.44 110.05 2892 − 1.8 9.9 − 11 20.6
24.9
32 R Hellroaring II 20 24 45.04 109.45 2892 − 1.4 10.3 − 10.4
21.7 36.5
33 R Sawtel Peak 26 18 44.54 111.41 2400 − 0.1 12.9 − 11.9
25.9 45.8
34 R Vinegar Hill 14 26 44.72 118.57 2338 0.5 11.4 − 8.6 39.8
54.1
35 S Mt. Hood 20 22 45.39 121.66 1969 1.7 10.8 − 4.9 48.4 15
36 S Newberry Crater 30 13 43.72 121.23 2100 2.9 12.9 − 4.7
45.5 58.1
37 S Paulina Peak 18 11 43.69 121.25 2250 2 11.9 − 5.3 42.3
59.6
38 S Batchelor Butte 16 1 43.26 122.68 2200 1.9 10.7 − 3.9
44.1 40
39 S Tipsoo Peak — 13 43.22 122.04 2462 0.7 10 − 5.4 34.4
35.3
40 S Moon Mtn. 6 4 43.20 122.65 2201 1.9 10.8 − 3.9 43.9 45.2
41 S Pelican Butte 40 17 42.51 122.15 2462 1.1 10.4 − 5 35.2
50.6
42 S Ball Mtn. 22 15 41.80 122.16 2363 2.2 11.4 − 4.4 39.1
107.1
43 S Goosenest Summit 6 6 41.72 122.23 2506 1.5 10.6 − 4.6
35.3 99.3
44 S Drakes Peak 26 16 42.30 120.15 2462 2.5 13.1 − 5.3 48.1
80.3
45 S Crane Mountain 19 22 42.07 120.24 2538 2.2 12.7 − 5.6
46.3 72.4
46 S Mt. Rose 11 — 39.30 119.90 2754 2.4 12.6 − 4.8 61 88.4
47 S Stevens Peak 7 — 38.70 119.98 2923 1.6 11 − 5.2 46.6

32. 61.6
48 S Ebbetts Pass 2 — 38.50 119.80 2769 2.5 12 − 4.4 46.9 72
Notes: Region: N = northern, R = Rocky Mountain, S =
southern; Lat. = latitude, Long. = longitude, Elev. = elevation,
See Table 3 for abbreviations and
explanation of variables.
69January 2008] BOWER AND AITKEN — ECOLOGICAL
GENETICS OF WHITEBARK PINE
treatment had fewer replications, lack of cold injury testing, and
the absence of
a few key populations at the northern and southern ends of the
range compared
to the ambient treatment, thus only data from the ambient
treatment were used
in the canonical correlation analysis. Climatic data and least-
squares population
means for each seedling phenotypic trait demonstrating signifi
cant ( P ≤ 0.05)
population differentiation were included in this analysis.
Canonical redundancy
analysis was used to determine the proportion of variation in
phenotypic traits
accounted for by canonical correlations with the climatic or
geographic data
sets. To assess potential differences in relationships between
seedling pheno-
typic traits and climatic variables between the two soil
temperature treatments,
canonical correlation analysis was repeated for the two
treatments separately
using only the populations common to both.

33. To develop predictive equations for the construction of seed
transfer guide-
lines, values of signifi cant canonical variables for the seedling
phenotypic traits
were regressed on the standardized climatic variable with the
highest loading
for that canonical variable. The slope of this regression
estimates the rate of
change in the phenotypic canonical variable relative to the
selected climatic
variable. Rates of differentiation along climatic gradients were
interpreted rela-
tive to the least signifi cant difference among populations at the
20% level (least
signifi cant difference: LSD 0.2). This conservatively reduces
type II error (ac-
cepting no differences among populations when differences
actually exist) and
minimizes maladaptation risk accordingly ( Rehfeldt, 1991 ).
Values of LSD for
the phenotypic canonical variables were obtained from a
Duncan ’ s multiple
range test in PROC GLM using the model for testing variation
among popula-
tions described. The fl oating seed transfer model developed by
Rehfeldt (1991 ,
1994 ) was used to determine seed transfer guidelines for
restoration programs
of whitebark pine. The maximum recommended environmental
transfer dis-
tance between seed collection population and planting site was
calculated as the
difference in the standardized climate variable associated with
the LSD ( P =
0.20) value of the phenotypic canonical variable multiplied by

34. the standard
deviation of the climate variable. Univariate regressions of
climate variables on
latitude, longitude, and elevation were used to determine the
geographic dis-
tances associated with the rates of differentiation in climate
variables to make
simple seed transfer recommendations.
RESULTS
Soil temperature effects — Height increment and survival
were
signifi cantly greater, on average, in the cold treatment than in
the ambient treatment (least squares mean = 6.7 and 8.9 mm,
P = 0.04 for height increment and 66.9 and 82.3%, P <
0.001
for survival, in the ambient and cold treatment respectively).
Means for biomass traits were also greater in the cold treatment,
and the temperature treatment difference greater for root mass
than shoot mass, although the difference between treatments for
these traits was not signifi cant. The date of needle fl ush did
not
differ signifi cantly between treatments. Population-by-treat-
ment interaction was not signifi cant for any of the traits. The
foliage of seedlings in the cold temperature treatments gener-
ally appeared darker green and healthier than those in the ambi-
ent treatment. No treatment-specifi c geographic trends were
evident, and separate canonical correlation analyses of individ-
ual treatments yielded the same results.
Geographic patterns across the species range — In general,
seedlings from populations originating from colder climates
had less overall growth, earlier needle fl ush in spring, and less
cold injury in fall than seedlings originating from milder cli-
mates when grown in the common garden. Populations differed

35. signifi cantly in the ambient soil temperature treatment for all
variables except root : shoot ratio and spring cold injury (
Table
5 ). Despite a lack of signifi cant differences among
populations
in the ANOVA, root : shoot ratio differed signifi cantly
among
populations in a Duncan ’ s multiple range test.
Growth-related traits generally had low levels of population
differentiation (0 ≤ Q ST ≤ 0.14), while the cold-adaptation
re-
lated traits (date of needle fl ush and fall cold injury) showed
obtained from a model using the thin plate splines of
Hutchinson (2000) as il-
lustrated for North America by McKinney et al. (2001) . Clines
in quantitative
traits can be obscured when there are correlations among traits
or if geographi-
cal structure is complex. In these cases, canonical correlation
analysis is more
effi cient than regressing each trait on environmental variables
separately ( West-
fall, 1992 ). Several of the seedling phenotypic traits and
climatic or geographic
variables were strongly intercorrelated ( Table 4 ), so canonical
correlation anal-
ysis was used to examine the relationships among these
variables. The cold
Fig. 1. Distribution of Pinus albicaulis and locations of
populations
tested in common-garden experiment. Dashed lines separate the
southern,

36. Rocky Mountain and northern regions.
TABLE 3. Description of (A) quantitative and (B) climatic
variables.
A) Quantitative trait Abbreviation Unit
3rd year height increment HTINC millimeters
Total dry biomass TDM grams
Root dry biomass RM grams
Shoot dry biomass SM grams
Root : shoot ratio RSR unitless
2003 Date of needle fl ush FL03 days from Jan. 1
2004 Date of needle fl ush FL04 days from Jan. 1
Fall cold injury FCI index of injury (%)
Spring cold injury SCI index of injury (%)
B) Climatic variable
Mean annual temperature MAT ° C
Mean temperature, warmest month MTWM ° C
Mean temperature, coldest month MTCM ° C
Mean annual precipitation MAP millimeters
Mean summer precipitation MSP millimeters
Annual heat : moisture index AH:M [(MAT +
10)/(MAP/1000)]
Summer heat : moisture index SH:M [(MWMT/(MSP/1000)]
Frost-free period FFP days
70 AMERICAN JOURNAL OF BOTANY [Vol. 95
nifi cant ( P = 0.006) and accounted for an additional 15% of
the
variation. The second pair of variables demonstrates the posi-

37. tive relationship between the length of the frost-free period and
growth, both height and biomass ( Table 6 ). The regression of
the second phenotypic canonical score on frost-free period was
also signifi cant ( P = 0.001) but weak ( r 2 = 0.24).
Canonical re-
dundancy analysis showed that the fi rst two climatic canonical
variables account for 24 and 17% (41% total) of the variation in
population phenotypic trait means, indicating substantial ge-
netic structure along climatic gradients.
Regional patterns of variation — When populations were
analyzed separately by region, some broad-scale geographic
differences in patterns of population differentiation emerged
( Table 7 ). In the ambient soil temperature treatment, in the
northern region, signifi cant differences were detected among
populations for all three biomass traits. In the Rocky Mountain
region, only date of needle fl ush in 2004 varied signifi cantly
among populations, while in the southern region, only date of
moderate to strong differentiation among populations regard-
less of treatment (0.36 ≤ Q ST ≤ 0.65). A comparison of Q
ST
values with previously published values of F ST for whitebark
pine ( Table 1 ) shows that the phenotypic traits with the
weakest
differentiation are similar to the highest estimates of differen-
tiation in presumably neutral molecular markers from rangewide
studies ( Jorgensen and Hamrick, 1997 ; Richardson et al.,
2002b ), and the quantitative traits with the strongest
differentia-
tion have substantially higher Q ST estimates.
In the canonical correlation analysis of population means for
seedling phenotypic traits and climatic variables for population
origins, the fi rst canonical correlation was signifi cantly differ-
ent from zero ( P < 0.0001) and explained 72% of the variance

38. in the data. The fi rst pair of canonical variables summarizes
relationships between cold-related phenotypic traits and mean
temperature of the coldest month ( Table 6 ). Mean temperature
of the …
A brief primer on statistical analysis
For this lab, you will be performing statistical analyses, with
the help of some Excel tools. If you have already taken
FRST231 or are presently taking it, this should be a refresher
for you. Otherwise, you may not have ever performed statistical
analysis, and will come across some confusing terms and
numbers you may not know how to interpret. Hopefully this will
clear up some confusion.
Why do we need to use statistics?
In this lab, we are seeking to explain genetic variation in tree
growth by comparing measurements across taxonomic varieties,
and analysing the relationship between our measurements and
the climates of our trees’ provenances. When collecting data,
there will always be some degree of unaccounted-for variation
in our measurements. This variation may be caused by
measurement error, natural variation caused by genetic
differences, environmental variation, or more likely some
combination of all these. As a result, real relationships are
“messy”. Below are two figures showing a hypothetical
relationship between provenance temperature and heights
measured in a common garden. While not perfect, figure 1
clearly shows a strong relationship between these variables. But
what about figure 2? It’s not as clear. Statistics allow us to ask
“How likely is it that the pattern we’re seeing is the result of a
real underlying relationship, rather than just random chance?”
We can quantify this likelihood and assess the probability that
our results reflect real relationships.
In this lab we will be performing two different statistical
analyses: linear regression, and t-tests. Linear regression is
a method to determine whether a relationship exists between
two numeric variables, e.g., height and provenance mean annual

39. temperature, as shown in figures 1 and 2. A t-test is used to
determine whether a relationship exists between one numeric
variable and one categorical variable, e.g., height and
taxonomic variety, as shown in figures 3 and 4.
Figure 2: The effect of provenance mean annual precipitation on
root-to-shoot ratios in 5-year old Pseudotsuga menziesii trees
grown in a common garden in Campbell River, BC (n=5-7 per
provenance).
Figure 1: The effect of provenance mean annual temperature on
height in 7-year old Pinus contorta trees grown in a common
garden in Prince George, BC (n=10 per provenance).
p-values
The cornerstone of most statistical analyses is the p-value. The
p-value is a statistic that reflects the probability that an
observed trend could happen by random chance, and it varies
from 0 to 1. If p is close to 0, there is a very low chance that the
pattern we see is due to chance (i.e., it is likely to reflect a real
relationship). If p is close to 1, there is no difference between

40. our data and data generated at random, so we should not
interpret there to be a relationship here. Figure 1 shows a trend
with a p-value of 0.0001, meaning that if we randomly
generated 10,000 datasets, we could expect one of them to show
a trend this strong. Figure 2 shows a trend with a p-value of 0.1,
meaning that random data could create a pattern at least as
strong as this 10% of the time.
We set an arbitrary threshold on p-values to assign statistical
significance to a trend, meaning we are willing to accept some
probability that our results are due to chance, but that number is
generally low. In most scientific fields, we set a significance
threshold of 0.05, meaning we are willing to accept a 5% chance
that our results are due to random chance. A p-value less than
0.05 means we can consider that result "statistically significant"
and therefore a reflection of a real pattern in the data. By this
criterion, figure 1 shows a statistically significant relationship,
while figure 2 does not. P-values are used in both linear
regression and t-tests. If a linear regression has a p-value less
than 0.05, we say that that the variables used in that analysis
have a significant relationship. If a t-test has a p-value less than
0.05, we say that the two groups we are comparing show
significant differences.
Linear regression
Linear regression is a commonly-used method across many
fields of biology. In this method, we are comparing data with
two dimensions: an independent variable (generally meaning
one that we assign or that is known ahead of time), and a
dependent variable (generally one that we measure, also known
as a response variable). In this lab, our independent variables
will be some aspects of climate from various provenances of
Garry oak. Our dependent variables will be tree heights and
diameters. Linear regressions are always presented as scatter
plots, where the independent variable goes on the x-axis (the
horizontal axis of the scatter plot), and the dependent variable
goes on the y-axis (the vertical axis of the scatter plot). In this
way, we can interpret the way that our dependent variable

41. changes in relation to our independent variable i.e., in figure 1
if the provenance mean annual temperature changes by 1°C,
how much does tree height change? This is the relationship
quantified by linear regression.
Linear regression uses a set of equations to draw a line of
fit through our data. This is shown by the grey diagonal lines in
figures 1 and 2. A line of fit is a straight line that best describes
the relationship in the data, and can be used to quantitatively
describe that relationship using its slope.For example, the linear
regression of figure 1 has a slope of 1.17m/°C, meaning for
every 1°C that provenance mean annual temperature increases,
we can expect height to increase by 1.17m. Another important
aspect of linear regression is called the coefficient of
determination, which has the mathematical symbol R2. This is a
measurement of how strongly the two variables in our
regression relate to one another. R2 ranges from 0 to 1, with 0
meaning there is no correlation between our data, and 1
meaning that the data is perfectly correlated i.e. all of the points
in our scatter plot would fall exactly along the line of best fit.
The relationship in figure 1 has an R2 of 0.62, which can be
interpreted as “provenance mean annual temperature explains
62% of the variation in mean provenance heights”. Both slope
and coefficient of determination are quantitative descriptions of
significant relationships. Therefore, they should not be reported
or interpreted for regressions with p-values greater than 0.05.
t-tests
Often, we wish to know whether two groups differ for some
variable of interest. In this lab, we will be determining whether
two taxonomic varieties of Garry oak differ in their average
height or number of stems. A simple statistical method for this
is called the t-test. A t-test determines whether the means of
two groups of data are significantly different. This is
determined not only by the means of those two groups, but the
amount of variation in each group. This variation is referred to

42. as error in statistics, although that term is misleading. As
mentioned earlier, variation in our data is expected even if we
measure things perfectly. We will quantify this variation in our
groups using a parameter called standard deviation. This
number is the average amount that each measurement in a group
differs from the mean. For example, in figure 3, the mean height
for Pinus contorta var. latifolia individuals is 9.9m, with a
standard deviation of 1.1m. This means that, on average,
individuals range from 8.8-11m within this group (the upper and
lower end of the error bars in this figure).
Figure 4: Belowground biomass of 5-year old Pseudotsuga
menziesii trees grown in fertilized(n=40) and unfertilized(n=35)
plots in a common garden in Campbell River, BC. Error bars
represent standard deviation.
Figure 3: Mean heights of 7-year old Pinus contorta var.
latifolia (n=100) and P. contorta var. contorta (n=40), grown in
a common garden in Prince George, BC. Error bars represent
standard deviation.
In figures 3 and 4, the groups both appear to differ. Pinus
contorta var. latifolia individuals appear taller than var.
contorta (fig. 3), and fertilized Pseudotsuga menziesii appear to

43. grow more roots in fertilized plots (fig. 4). However, the error
bars in figure 4 are larger than those in figure 3, meaning the
data in figure 3 has more variation. A t-test can tell us whether
these differences are statistically significant. The outputs of a t-
test are a t-value and a p-value. The t-value is difficult to
interpret without a much deeper dive into statistics, but a low
value (near 0) represents less difference between groups than a
larger value. However, the p-value is the same as discussed
earlier. If p < 0.05, then the groups are significantly different, if
p > 0.05, the groups are not significantly different. In figure 3,
p = 0.001. These groups are significantly different. In figure 4,
p = 0.09. These groups do not differ significantly. If our groups
differ significantly, we can discuss the relative difference
between theme.g., in figure 3, var. contorta individuals have an
average height of 8.5m, 15% shorter than var. latifolia
individuals. If the groups do not differ significantly, we infer
that there is no difference between the mean values of our two
groups and should not attempt to quantify this.
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How to Write a Lab Report
Forest Biology (FRST 200 and 210)
Read this document carefully, it outlines the content and
formatting that should be used for
lab reports in FRST 200 or 210 and will be the basis for our
marking system. You should note that
marking for your first lab report will be reasonably relaxed, but
in the second and subsequent reports
the number of marks deducted for mistakes will increase
substantially. This system encourages you
to rapidly adopt a simple, clear and systematic style of
scientific writing. The easier your report is to
read the more likely you are to get good marks.
1. Report Purpose

46. You should aim to produce a clear, concise report that reviews
current knowledge on the
subject, develops the research objectives, summarizes the
methods used, and presents the results
generated. You should then discuss how the results meet the
objectives and what they mean relative
to the current knowledge or literature that was used to develop
your objectives, which may include
applications to which this knowledge is relevant.
2. Report Structure & Content
Use the following sections to structure your report. Pay close
attention to the section details
and suggested content. All sections should be concise and
informative.
Introduction
The introduction provides readers with the background
information required to understand
the whole report. Introductions are funnel-like, starting broadly
with an introductory sentence that
introduces the report’s context, and ending narrowly with a
clear definition of specific objectives or
questions being investigated in your report (example 1). We
generally leave objectives open-ended,

47. so you can tailor the objectives to your interests e.g., wood
production, conservation, biology.
Relevant background information fills the funnel, setting up any
objectives of the study, and any
hypotheses you wish to test that will be covered in your
discussion section.
Example 1: Your report is comparing the wood quality of two
timber species, Picea abies and Pinus
sylvestris. A good introductory sentence could be, “Wood
quality, a trait in which many species vary
substantially, is an important determinant of timber value.”
Your introduction then should provide
some background information that would be relevant to the
reader, and narrows the focus from
wood quality in general down to more specific aspects.
Potential topics might include: economic
value of high-quality wood; features that contribute to wood
quality (especially whichever ones you
measured); or variability in these features. This can be further
narrowed to the species being
studied: compare and contrast life histories, growth rates,
relative importance to the timber industry,
etc. Now that the reader has some knowledge of wood quality
and your species, you can establish

48. your objectives i.e., the question your study is trying to answer:
“While Picea abies and Pinus
sylvestris are both important to European timber production, the
quality of their mature wood has not
been adequately assessed." Your last sentence should briefly
summarize what you’ve done to
address your objective: “In this study, differences in tracheid
length and extractives content were
compared between Scots pine (Pinus sylvestris L.) and Norway
spruce (Picea abies (L.) H. Karst.).”
Assume that your reader is educated, but unfamiliar with your
subject. Therefore, the
introduction should also define and contextualize any
terminology that is fundamental to your study.
To do this you will need to concisely summarize relevant
information from class notes and include
background research. In most cases, this will include citing
relevant scientific literature. Do not
include any methods, results or conclusions in your
introduction.
Your introduction section should conclude with your
hypotheses. These are the scientific

49. statements that you wish to test in your lab report. You should
provide an explanation of why you
developed your hypotheses, followed by a list of your
hypotheses. These hypotheses are the basis of
your discussion. Your hypotheses must be testable, and provide
a predicted direction of the
relationship (example 2). This will allow you to later assess
whether your hypotheses were supported
or refuted by your data.
Example 2: For the previously mentioned research topic, a good
hypothesis section might be as
follows: “As Scots pine generally has a higher growth rate than
Norway spruce, it was expected that
Scots pine wood will exhibit more traits consistent with fast-
growing species. As such, the authors
predicted that Scots pine wood would have longer tracheids than
Norway spruce, more earlywood,
and lower wood density”.
Materials and Methods
The materials and methods is an account of how your
experiment was conducted, and why it
was conducted this way. This should concisely provide enough
information so that your experiment

50. could be repeated exactly. Materials and methods must be
written in clear sentences, not in point
form. Materials should be incorporated into the methods, not
included separately (example 3). Be
sure to outline all the different types of data collected, the
measurements that were made, and the
units of measurement used. Also include information regarding
your data analysis. How was your raw
data used to answer your hypotheses? Describe any drawings
that were made and their purpose (do
not include the actual drawings here). If you used any scientific
equipment, try to provide as much
information about it as possible (e.g., equipment name and
model number; example 3). If you used
software to analyze your data, include what you used and which
version. Any relevant equations that
were used to transform your data must be included within your
methods.
Do not include unnecessary details about how the lab was
performed. Remember that this is
meant to be an account of how your experiment was conducted,
not your forestry lab. For example,
another scientist does not need to know that you collected data
in groups of three or that the TA

51. compiled your class data. Do not copy the lab instruction sheet
which describes what you should do
during the lab, not what you did during the experiment. You
should summarise the methods in your
own words.
Example 3: Good methods might read “1 cm2 cubes of mature
Picea abies and Pinus sylvestris wood
was collected from centre-cut commercial-grade dimensional
lumber. To obtain tracheid samples,
these cubes were pulverized individually using an Ailence MF-
1000 wood pulveriser.” Bad methods
would be “We were given small pieces of wood from the
species. The TAs put them in the wood
pulveriser for us.”
Results
Use the results section to summarize your data and report
observed trends. Do not discuss
how, why, or what may be the basis for these trends (this
material belongs in the Discussion section).

52. Results should be quantitative rather than qualitative whenever
possible. If you are comparing
different groups, your results should compare these groups
(example 4). If your results are
presenting a correlation between measurements, include the
direction and relative strength of the
relationship (e.g. slope or R2; example 5). When giving
quantitative results, always include units.
Example 4: An example of a result that is both quantitative and
comparative is, “Mean tracheid
length in Scots pine was estimated to be 2.57mm (standard
deviation: 0.57mm). Tracheids of
Norway spruce were, on average, 35% longer, with a mean of
3.47mm (standard deviation: 0.8mm).”
A bad example of a result, that is neither sufficiently
quantitative nor comparative would be,
“Tracheids in Scots pine were 2.57. Tracheids in Norway spruce
were a lot longer.”
Example 5: A good result describing a correlation is “As
distance of the wood sample from the pith of
the tree increased, extractives percentage decreased linearly in
Norway spruce wood (slope = 0.14
µLexactives cm-1 ; R2=0.73).” A bad example that relies of
qualitative descriptions would be “Distance of

53. the wood sample from the pith of the tree and extractives
percentage were found to be related. The
relationship was very strong.”
Your results sections will also include tables or graphs that
display notable trends in your
data. See ‘Writing and Formatting Rules’ below for instructions
on how to present and refer to tables,
figures and appendix items. Drawings or photographs can be
essential to your results for some
reports, and they may be included in the results section or the
appendix at your discretion.
Discussion
Your discussion is where you address your hypotheses (from the
Introduction), and
systematically explain possible reasons behind all trends
reported in the results section (even if they
are not explicitly included in your hypotheses), as well as
limitations of the methods used. Writing the
discussion always involves using additional sources of
information (i.e., references from the scientific
literature or from texts, not course notes), statistical
interpretation (if any), deductive reasoning, and
logical inference to explain the results that you reported. For

54. each trend a simple procedure is to
make a statement of the trend you observed, then follow this
statement with explanations of why you
believe this trend might have occurred (example 6).
Example 6: A good discussion sentence stating an observed
trend could be, “Although there is large
variability in the data, Norway spruce tracheids were found to
be generally longer than those of Scots
pine.” After making the statement, you could discuss possible
explanations for why Norway spruce
produced longer tracheids in this experiment, ideally supported
by outside scientific literature e.g.
“An analysis of the wood from seven spruce and pine species
found that spruces tend to produce
longer tracheids than pines (Anoulli et al. 1986). This suggests
that our trend may be due to broad
differences in growth strategy that have evolved between the
two genera, and is unlikely to be
unique to our specific samples.”
In general, a single study cannot prove or disprove a hypothesis.

55. Often we are using a small
sample and assuming it is representative of a broader group.
Our measurements are imperfect, and
it is possible that no causal relationship exists between our
variables, but rather they are both
related to a third underlying, unobserved variable, or just due to
random chance. As such, we can
only use our data to support or refute a hypothesis, rather than
prove or disprove it.
Your discussion should also reflect on any limitations or
sources of error in your data. Make
notes as you perform your experiment or measurements about
what aspects seem more or less
precise or accurate. Unless you can describe very specific errors
in your dataset, the rest of your
discussion should treat your results as though they are accurate
and generalizable. Saying that “this
trend exists because our data is probably wrong” is rarely a
valid explanation.
A discussion should also include some broader implications or
applications of your results
i.e., why should we care about these results? We will usually
ask you questions in the lab handout
that attempt to draw these connections, and these should be

56. answered within the discussion. While
it is often useful to look into the scientific literature to answer
these questions, remember to draw
from your own results as well (example 7).
Example 7: If one of the questions for this hypothetical lab was
“Which species’ wood would be more
appropriate for building a house?” a good response would be,
“Tracheid length has been found to be
strongly correlated with microfibril angle, a critical parameter
of wood strength (Kennedy 1995). As
we found Norway spruce to generally have longer tracheids than
Scots pine, it is possible that
Norway spruce has a lower microfibril angle as well and
therefore produces better wood for home
construction. This is supported by the results of Lichtenegger et
al. (1999), who directly measured
microfibril angle in these species.” A bad response would be
“Previous studies have found that
Norway spruce has stronger wood than Scots pine (Lichtenegger
et al. 1999). Therefore I would use
Norway spruce to build a house”. While this technically answers
the question, it does not relate to
any of the results obtained and therefore would be out of place
in this discussion.

57. Your discussion should end with a brief conclusion. This is a
concise summary of the report’s
main findings, why they occurred, and what their further
implications are.
References
List in alphabetical order (according to first author's last name)
all the sources that you have
cited in your report (see the ‘Writing Rules and Formatting’
section for information on using
references). These references should be, in most cases, peer-
reviewed scientific literature or
accepted text books. Do not cite course notes unless you are
given explicit permission. If you cite a
source that does not appear in the references, or provide a
reference for a source that is never cited,
you will not receive credit for this reference.
Appendix
The appendix contains data and figures that do not belong or fit
in the rest of the report. Raw
data sheets, sampling maps, or large drawings are common
appendix items. They should appear in
the appendix in the same order that they are referred to in the
text. Only include items in the

58. appendix if they are mentioned in the report and cite them in the
same style as tables or figures. To
do this, each appendix item needs to be clearly numbered. Not
all reports require an appendix.
3. Writing and Formatting Rules
General Writing Etiquette
Proper paragraph formatting, clear sentence structure, good
spelling and grammar are basic
expectations for all assignments. If you are not confident in
your writing ability, it is a good practice to
have a friend who is proficient with English proofread your
report. Reports must be typed, with size
11 or 12 font, 1.5 or 2 line spacing, and 2.5 cm margins. Do not
include a cover page for your report.
Using Latin and Common Names
The first time a species name is used, include the whole Latin
name and authority e.g.,
Pseudotsuga menziesii (Mirb.) Franco as well as the common
name e.g., Douglas-fir. As long as there

59. is no potential for confusion, the genus may be abbreviated
following the first use e.g., P. menziesii
or the common name may be used throughout the write-up.
Latin names (in fact all non-English
words) must always be italicized or underlined. Common names
do not use capitals unless they
include proper nouns such as a place or person's name (e.g.,
black spruce, Sitka spruce and
Douglas-fir are all correct, whereas Black Spruce, sitka spruce
and douglas-fir are all incorrect).
Citations and References
Citing and referencing appropriate literature is an essential
component of all scientific
reports. We expect you to use peer-reviewed scientific papers in
journals for your reports. You can
find these by using search engines such as Web of Science
(available through the UBC Library) or
Google Scholar, and access them through the UBC Library. A
citation is required whenever you use
information from somebody else’s text or their ideas. A
‘citation’ is the mention of literature in the
main body of your text, whereas a ‘reference’ is the full
bibliographic information for a given citation
that is listed in the reference section. You should use the

60. “(author date)” citation style (examples 6,
7). This should go at the end of the sentence using the source
information, unless you are referring
to the authors within the sentence (example 7). For reference
format, you can use any established
style (e.g., MLA, APA, Chicago) as long as you are consistent
within your references. Don’t just copy
and paste references as you find them. They are frequently
automatically generated and contain
incorrect or insufficient information.
When citing information, paraphrase the source material
(example 8). Do not quote or
directly copy-paste text from references! Improper citation will,
at best, lose you many marks on your
assignment. At worst, you could end up facing expulsion from
UBC for plagiarism. Directly copying 7
or more consecutive words from any source material is
considered plagiarism by UBC standards.
There are some rare cases where quoting information is
acceptable. Generally this is when it is an
author’s specific opinion or their exact sequence of words that
are relevant, or if they are providing a
technical definition (example 9).

61. Example 8: Here is a piece of source information, from
Kennedy (1995): “There are a host of
properties that determine the quality of wood for different
purposes, but the single most important
characteristic is wood specific gravity or relative density.” An
appropriate citation using this
information could be:
any properties, but relative
density is the most important
characteristic (Kennedy 1995).
The following are improper uses of this information:
wood for different purposes, but
the single most important characteristic is wood specific gravity
or relative density” (Kennedy
1995).

62. wood, but the most important is
wood relative density (Kennedy 1995).
f properties that determine the quality of
wood.
Note that the last 2 examples are explicit forms of plagiarism
and may lose you full credit for the
assignment or worse. Even if you are citing a source, you must
paraphrase their content.
Example 9: This is an appropriate use of quotation for a
reference: “Charles Darwin was among the
first to link domesticated crops and livestock to natural
evolution, claiming that he ‘invariably found
that our knowledge, imperfect though it be, of variation under
domestication, afforded the best and
safest clue’ to understanding natural selection (Darwin 1859).”
Measurement Units
Report your data using appropriate SI i.e., metric units and
always give the units of
measurement for any data when it is first mentioned in your
report as well as in every table or figure.
Think carefully about what your measurements mean in reality
to determine the appropriate number
of decimal places or significant figures that should be used.

63. Programs like excel will often provide
numbers to 10 decimal places, far more precise than the data
that you input. Using these numbers
as they are given is misleading your reader into thinking your
results are more precise than they truly
are.
Figures and Tables
Figures such as graphs, drawings and photographs are tools for
illustrating written content
that is not otherwise easy to visualize. Tables present values
that summarise raw data across
several categories or variables. In your FRST 200 & 210
reports, tables and figures are essential
requirements because formal statistical analysis is usually not
required, but we still expect you to
identify and discuss trends.
When creating a figure or table ask yourself what you’re trying

64. to demonstrate to the reader and stick
to the following rules:
–we will not be impressed. We are
looking for figures that are
simple, clear and informative.
analysis lab (FRST 210) should be
word processed.
–don’t use small figures. As a guideline, graphs
should be full-page-width. Table
size depends on the amount of data, if the table doesn’t fit on a
single page it belongs in the
appendix.
eparate entities–do not label tables as
figures.
This information is provided by
the caption.
the figure, but captions for
tables are placed above the table.

65. the units of measurement.
graph if they have identical units
of measurement and values in the same order or magnitude.
and make sure that the colours
or symbols are organized such that the reader can draw fast
visual conclusions about each
trend.
bar and must be clearly labelled
with any parts described in your
report.
means calculated from multiple
measurements or groups, only provide the means. You can
include the data used to generate
the means as a separate table in the appendix.
Figures and tables must be cited and explained in the results
section. It is best to cite a
figure or table in parentheses at the end of the respective
sentence (see example 10). It is never
acceptable to simply list attached figures or to say “see attached
figures”. All figures and tables

66. should be positioned in the report close to the relevant
paragraph and in the same order that they
are mentioned in the text, so the reader is not confused trying to
find the correct figure.
Example 10: A good citation of a figure would be “Mean
tracheid length in Scots pine was estimated
to be 2.57mm. Tracheids of Norway spruce were, on average,
35% longer, with a mean of 3.47mm
(figure 1).” A bad example would be “Figure 1 shows that mean
tracheid length in Scots pine was
estimated to be 2.57mm. Figure 1 also shows that tracheids of
Norway spruce were, on average,
35% longer, with a mean of 3.47mm.”
Every figure and table must have a caption that includes a
number and a brief written
description (in complete sentences) of what the figure contains,
but doesn’t describe any trends
(example 11). Captions should be informative enough that you
could use them to understand the
figure or table in the absence of the rest of your report. If you
use mean values, provide the sample
size used to generate your means (example 11). If you use error
bars, say what type of error was

67. used.
Example 11: A good example of a figure caption might be
“Figure 1: Tracheid lengths of mature wood
from Picea abies (n = 5) and Pinus sylvestris (n = 7). Error bars
are given in standard deviation.”
Bar Graphs, Line Graphs and Scatter plots: Your chosen graph
should be determined by the type of
data on your x-axis. Bar graphs are only used when the x-axis is
categorical (e.g., “pines” vs.
“spruces”), but line graphs are required whenever the data are
continuous and show change from
one measurement point to the next (e.g., latitude, longitude or a
time series such as day of year).
Scatterplots are used to display numerous data points for
continuous x-and y-axes. Data points on
line graphs can be connected, but never use a smoothing
function in your lab reports. Points in
scatterplots should never be connected with lines, but if you
want to show a general trend in your
data, you can insert a line of best fit through the data points
(called a trend line in Excel).
4. Useful Hints and Tips

68. The secret to writing great lab reports: Your first lab reports
can be daunting and challenging to
write, but once you’re familiar with the format they will become
much easier and obtain higher
marks. We recommend writing the materials and methods
section first because it’s simple,
straightforward and familiar to you. Next, analyse your data,
create your graphs and write the results
section. Then write the discussion. Finally, although it’s
counterintuitive, finish by writing the
introduction. This should make the content of the introduction
easier to judge and prevent it from
being too short or long.
Write concisely: Keep sentences fairly short, and don’t use long
words if a short one will do. A good
scientific writer will use the minimum number of words
possible to clearly state their point, so don’t
use sentences that don’t tell the reader anything meaningful. For
example, “This lab was interesting
and helpful for understanding germination better” tells the
reader nothing what you learned, so leave
it out. Avoid unnecessary words, e.g., the word “the” can often
be deleted with no change in
meaning. After writing a first draft, edit it so that the major

69. messages are maintained but the text is
shortened.
The meaning of ‘significant’: In science, ‘significant’ refers to
the outcome of a statistical test. We’re
not doing statistical tests in most of these labs, so it is best to
avoid using this term to refer to a
strong relationship in your data, unless you have actually tested
whether is constitutes a statistically-
significant difference.
Avoid making overly large scientific inferences: Extrapolation
of your data trends to larger scientific
concepts will be required; however, it is not acceptable to do so
without adequate supporting
information. For example, applying the concepts learned from
observing physiological differences
between Scots pine and Norway spruce to all firs and spruces
would be inappropriate. You need to
use valid scientific references to support any conclusions you
draw or speculations you make based
on your results. When you are suggesting possible explanations
for results, if those are speculative,
write it in a way that reflects the uncertainty, e.g., using words
such as “may result from”, or “could

70. be explained by”. If you want to know more about how to write
good scientific papers, there is lots of
information available online. Here is one particularly user-
friendly resource.
http://abacus.bates.edu/~ganderso/biology/resources/writing/HT
Wtoc.html
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