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I'm coming across an increasing number of papers that use trait-based approaches for risk assessment, like this one . This paper defines a trait as follows:
Traits are the physiological, morphological, and ecological attributes of species or other taxonomic entities, which describe their physical characteristics, ecological niche, and functional role within an ecosystem.
I've wondered about using this sort of approach during some analyses of invasiveness potential in vector insects before and it's likely to come up during a systematic literature review I'm coordinating, but I'm concerned that devising my own trait classification system is likely to introduce confirmation bias into a study. Is there either a recognised methodology for trait definition, or alternatively some sort of accepted list or classification of traits that I could use, to minimise the chances of this? Is there a generally-accepted method to correct for phylogenetic correlation?
I've tried Google but the results mostly appear to be about trait theory, which is an approach used in psychology to study human personality.
I think you this paper is what you need. The many concepts of trait are discussed in the functional ecology background. But basically, a "trait" can be defined as
" 'Functional traits' are defined as morpho-physiophenological traits which impact fitness indirectly via their effects on growth, reproduction and survival, the three components of individual performance." (Violle et al. 2007)
Which are not so different from the definition you posted. Some organisms have an improved methodology to collect and measure traits, like plants. But this is not common to all organisms. As for the method to correct for phylogenetic correlation, this is a whole different field called Phylogenetic Comparative Methods. To choose the method you need to know the kind of data you have and the question being asked.
THE THREE DIMENSIONS OF SCIENCE LEARNING
Within the Next Generation Science Standards (NGSS), there are three distinct and equally important dimensions to learning science. These dimensions are combined to form each standard—or performance expectation—and each dimension works with the other two to help students build a cohesive understanding of science over time.
Crosscutting Concepts help students explore connections across the four domains of science, including Physical Science, Life Science, Earth and Space Science, and Engineering Design.
When these concepts, such as “cause and effect”, are made explicit for students, they can help students develop a coherent and scientifically-based view of the world around them.
Science and Engineering Practices
Science and Engineering Practices describe what scientists do to investigate the natural world and what engineers do to design and build systems. The practices better explain and extend what is meant by “inquiry” in science and the range of cognitive, social, and physical practices that it requires. Students engage in practices to build, deepen, and apply their knowledge of core ideas and crosscutting concepts.
Disciplinary Core Ideas
Disciplinary Core Ideas (DCIs) are the key ideas in science that have broad importance within or across multiple science or engineering disciplines. These core ideas build on each other as students progress through grade levels and are grouped into the following four domains: Physical Science, Life Science, Earth and Space Science, and Engineering.
GET TO KNOW
The Next Generation Science Standards (NGSS) are K–12 science content standards. Standards set the expectations for what students should know and be able to do. The NGSS were developed by states to improve science education for all students.
A goal for developing the NGSS was to create a set of research-based, up-to-date K–12 science standards. These standards give local educators the flexibility to design classroom learning experiences that stimulate students’ interests in science and prepares them for college, careers, and citizenship.
QUALTIY NGSS INSTRUCTIONAL UNITS
RUBRIC FOR LESSONS AND UNITS
MIDDLE AND HIGH SCHOOL COURSE MAPPING
Figure 16.1 “Are you an introvert”? In popular culture it’s common to talk about people being introverts or extroverts as if these were precise descriptions that meant the same thing for everyone. But research shows that these traits and others are quite variable within individuals.
When we observe people around us, one of the first things that strikes us is how different people are from one another. Some people are very talkative while others are very quiet. Some are active whereas others are couch potatoes. Some worry a lot, others almost never seem anxious. Each time we use one of these words, words like “talkative,” “quiet,” “active,” or “anxious,” to describe those around us, we are talking about a person’s personality—the characteristic ways that people differ from one another. Personality psychologists try to describe and understand these differences.
Although there are many ways to think about the personalities that people have, Gordon Allport and other “personologists” claimed that we can best understand the differences between individuals by understanding their personality traits. Personality traits reflect basic dimensions on which people differ (Matthews, Deary, & Whiteman, 2003). According to trait psychologists, there are a limited number of these dimensions (dimensions like Extraversion, Conscientiousness, or Agreeableness), and each individual falls somewhere on each dimension, meaning that they could be low, medium, or high on any specific trait.
An important feature of personality traits is that they reflect continuous distributions rather than distinct personality types. This means that when personality psychologists talk about Introverts and Extraverts, they are not really talking about two distinct types of people who are completely and qualitatively different from one another. Instead, they are talking about people who score relatively low or relatively high along a continuous distribution. In fact, when personality psychologists measure traits like Extraversion, they typically find that most people score somewhere in the middle, with smaller numbers showing more extreme levels. Figure 16.2 shows the distribution of Extraversion scores from a survey of thousands of people. As you can see, most people report being moderately, but not extremely, extraverted, with fewer people reporting very high or very low scores.
Figure 16.2 Distribution of Extraversion Scores in a Sample Higher bars mean that more people have scores of that level. This figure shows that most people score towards the middle of the extraversion scale, with fewer people who are highly extraverted or highly introverted.
There are three criteria that are characterize personality traits: (1) consistency, (2) stability, and (3) individual differences.
- To have a personality trait, individuals must be somewhat consistent across situations in their behaviours related to the trait. For example, if they are talkative at home, they tend also to be talkative at work.
- Individuals with a trait are also somewhat stable over time in behaviours related to the trait. If they are talkative, for example, at age 30, they will also tend to be talkative at age 40.
- People differ from one another on behaviours related to the trait. Using speech is not a personality trait and neither is walking on two feet—virtually all individuals do these activities, and there are almost no individual differences. But people differ on how frequently they talk and how active they are, and thus personality traits such as Talkativeness and Activity Level do exist.
A challenge of the trait approach was to discover the major traits on which all people differ. Scientists for many decades generated hundreds of new traits, so that it was soon difficult to keep track and make sense of them. For instance, one psychologist might focus on individual differences in “friendliness,” whereas another might focus on the highly related concept of “sociability.” Scientists began seeking ways to reduce the number of traits in some systematic way and to discover the basic traits that describe most of the differences between people.
The way that Gordon Allport and his colleague Henry Odbert approached this was to search the dictionary for all descriptors of personality (Allport & Odbert, 1936). Their approach was guided by the lexical hypothesis, which states that all important personality characteristics should be reflected in the language that we use to describe other people. Therefore, if we want to understand the fundamental ways in which people differ from one another, we can turn to the words that people use to describe one another. So if we want to know what words people use to describe one another, where should we look? Allport and Odbert looked in the most obvious place—the dictionary. Specifically, they took all the personality descriptors that they could find in the dictionary (they started with almost 18,000 words but quickly reduced that list to a more manageable number) and then used statistical techniques to determine which words “went together.” In other words, if everyone who said that they were “friendly” also said that they were “sociable,” then this might mean that personality psychologists would only need a single trait to capture individual differences in these characteristics. Statistical techniques were used to determine whether a small number of dimensions might underlie all of the thousands of words we use to describe people.
Biological and ecological traits of benthic freshwater macroinvertebrates: relationships and definition of groups with similar traits
Relating species traits to habitat characteristics can provide important insights into the structure and functioning of stream communities. However, trade-offs among species traits make it difficult to predict accurately the functional diversity of freshwater communities. Many authors have pointed to the value of working with groups of organisms as similar as possible in terms of relationships among traits and have called for definition of groups of organisms with similar suites of attributes.
We used multivariate analyses to examine separately the relationships among 11 biological traits and among 11 ecological traits of 472 benthic macroinvertebrate taxa (mainly genera). The main objective was to demonstrate (1) potential trade-offs among traits (2) the importance of the different traits to separate systematic units or functional groupings and (3) uniform functional groups of taxa that should allow a more effective use of macroinvertebrate biological and ecological traits.
We defined eight groups and 15 subgroups according to a biological trait ordination which highlighted size (large to small), reproductive traits (K to r strategists), food (animal to plant material) and feeding habits (predator to scraper and/or deposit feeder) as ‘significant’ factors determining the ordination of taxa. This ordination partly preserved phylogenetic relationships among groups.
Seven ecological groups and 13 ecological subgroups included organisms with combinations of traits which should be successively more adequate in habitats from the main channel to temporary waters, and from the crenon to the potamic sections of rivers, and to systems situated outside the river floodplain. These gradients corresponded to a gradual shift from (1) rheophilic organisms that lived in the main channel of cold oligotrophic mountain streams to (2) animals that preferred eutrophic habitats of still or temporary waters in lowlands. The groups with similar ecological traits had a more diverse systematic structure than those with similar biological traits.
Monitoring and assessment tools for the management of water resources are generally more effective if they are based on a clear understanding of the mechanisms that lead to the presence or absence of species groups in the environment. We believe that groups with similar relationships among their species traits may be useful in developing tools that measure the functional diversity of communities.
Types of inductive reasoning
There are various ways to use inductive reasoning depending on the situation. Here are the three most commonly used types of inductive reasoning:
In this type of inductive reasoning, a situation is presented, you look at evidence from past similar situations and draw a conclusion based on the information available.
Example: For the past three years, the company has beat its revenue goal in Q3. Based on this information, the company will likely beat its revenue goal in Q3 this year.
This type of inductive reasoning utilizes statistical data to draw conclusions.
Example: 90 percent of the sales team met their quota last month. Pat is on the sales team. Pat likely met his sales quota last month.
In this case, you are using statistical evidence to inform your conclusion. While statistical induction provides more context for a possible outcome or prediction, it is crucial to remember new evidence may vary from past research and can prove a theory incorrect.
Induction by confirmation
Induction by confirmation allows you to reach a possible conclusion, but you must include specific assumptions for the outcome to be accepted. This type of inductive reasoning is used often by police officers and detectives. Here’s an example:
Renee broke into a building.
Anybody who breaks into a building will have opportunity, motive and means.
Renee was in the area and had lock picks in his bag.
Renee likely broke into the building.
In this situation, you develop a theory, and to prove it true, you must have specific evidence. Knowing that Renee was in the area where the building was broken into and had a lock pick in his bag are strong points to him being the one who broke into the building. Understanding the various types of inductive reasoning allows you to better implement them in your day-to-day operations within the workplace.
Searching for genes that explain our personalities
Identifying such genes could eliminate the distinction psychologists make between personality and psychopathology.
September 2002, Vol 33, No. 8
In fact, more researchers are jumping into the complex fray of behavioral genetics each year, fueled by the hope that identifying genes related to personality traits will not only help them better understand what makes people tick but also what goes wrong when normal "ticking" turns pathological.
The goal is to discover genes that affect brain functions that in turn affect how people interact with their environments. The research is slowed by the complexity of the search: Many genes are responsible for various aspects of people's temperament, and those genes appear to interact with each other in complicated ways that influence several traits at once--and then likely only in very subtle ways, with any one gene likely accounting for only 1 or 2 percent of the variance in a trait.
Researchers do, however, believe that their work will eventually pay off and they'll have a new, more comprehensive, understanding of personality and psychopathology as well as the complex play between genes and environment in shaping personality.
Progress to date
Scientists have a strong foundation for their search for personality genes from the years of basic psychology and neuroscience studies that have explored just exactly what personality is and how personality-related behaviors might be influenced by specific neural mechanisms. And although researchers still debate exactly how to define personality, they have identified certain core personality dimensions that are consistent across cultures, including novelty-seeking, neuroticism and agreeableness.
Intriguing to people has been research in animals and humans that links certain neurotransmitters with some of these dimensions or traits. For example, many studies have found a connection between high levels of the neurotransmitter dopamine and behaviors related to novelty-seeking. That gives researchers a place to start looking--genes related to dopamine--among the nearly 50,000 in the human genome.
To date, there are only two real candidate genes that anyone speaks of with any confidence. The first potential link is between some behaviors related to the Big-Five trait novelty-seeking and a gene that produces the protein responsible for creating a dopamine receptor called DRD4. While some studies have failed to replicate this connection, others have identified a link between the DRD4 gene and other traits linked to novelty-seeking, such as drug abuse and attention-deficit hyperactivity disorder. The indication is that this gene--or perhaps some other gene related to it--may influence all these interrelated characteristics.
The second candidate--linked to the Big Five trait neuroticism--is commonly called the "Prozac" gene because it produces a protein related to the neurotransmitter serotonin. Also known as the serotonin transporter gene or 5-HTTLPR, it has the strongest evidence linking it to neuroticism and other anxiety-related traits, such as harm avoidance.
Even so, the gene appears to account for only about 1 to 2 percent of the variance for these traits, says National Cancer Institute molecular biologist Dean Hamer, PhD, one of the first scientists to search for personality genes. "If that's as good as it gets," he says, "everything else is likely worse." That means perhaps hundreds of genes influence each of our personality traits ever so slightly.
In fact, the work is so difficult from a molecular biology point of view, Hamer is all but abandoning it.
"After 10 years or so, it's quite clear to me that at least for most traits there are a very large number of genes involved," he says. The only area he'll continue working on is sexual orientation. There he feels there's a better chance of finding just a few key genes.
Blurring lines between 'normal' and pathological
The difficulty of the work isn't stopping others who anticipate the promise of a greater understanding of personality as well as psychopathology. Already, research has begun to blur the traditional line delineating personality and psychopathology as separate entities.
For example, over the past decade, studies have established a connection between high scores on the standard personality trait of neuroticism and major depression. In fact, high neuroticism scores can predict whether someone will develop major depression, says Kenneth Kendler, MD, director of the Psychiatric Genetics Research Program at Virginia Commonwealth University, who conducted some of the research showing this link. Other studies by Kendler suggest that neuroticism and depression share as much as 60 percent of their genes. In fact, most researchers in this area expect they'll find that many of the genes that influence general personality also play a role in many forms of psychopathology.
Such findings would suggest that conditions such as depression, anxiety disorders and attention-deficit hyperactivity disorder are one end of a continuum that includes normal personality traits.
"Once we get genes for psychopathology, we'll get genes for personality" and vice versa, says Robert Plomin, PhD, deputy director of the Social, Genetic and Developmental Psychiatry Research Centre in the Institute of Psychiatry at King's College, London. "At least for more common disorders, such as hyperactivity, all the evidence points to a continuum of traits. Activity and hyperactivity are just variants of each other."
Understanding environment through genes
The research could also revolutionize how psychologists define psychopathology, which is currently diagnosed by symptoms, says Plomin.
"All our concern about diagnosis based on symptoms might be off base," he says. Instead, psychopathology could be defined and diagnosed based on genes and their interaction with the environment to produce certain outcomes. This would allow clinicians to detect people at risk for a certain disorder and, perhaps, prevent symptoms from ever occurring by modifying a person's environment.
Of course, the reality of using genetic markers to diagnose psychiatric disorders--not to mention to assess personality traits--is likely decades away. In fact, some researchers think it's unlikely because of the number of genes involved in any one trait.
"One can fantasize about replacing self-report inventories with genetic assays to assess personality traits," says psychologist Jeff McCrae, PhD, a personality psychologist at the National Institute on Aging (NIA), "but I doubt it will ever become a reality. The link between genes and traits is too imperfect, and we would need to discover all the genes associated with each trait and how they interact in order to come up with a gene-based personality assessment." More likely--and equally important for personality researchers--is the idea that they will be able to include genetic markers among the criteria they use to validate their personality measures.
"[Genetic markers] could provide one more objective indicator against which to evaluate our instruments," says McCrae.
In addition, finding genes is sure to help researchers better understand how environment and genes interact to shape personality. That's the idea behind research by McCrae and his long-time NIA collaborator Paul Costa, PhD. They have developed the Five-Factor Theory, which says that personality traits themselves are genetically based, but that characteristic adaptations--habits, beliefs, values, self-concepts, roles, relationships, skills--are shaped jointly by genetically determined traits and the environment.
Once they and other researchers pin down at least some of the genetics of the traits, they could much more easily evaluate the environmental contribution to these characteristic adaptations.
"For example," says McCrae, "we might find that people high in Gene A everywhere in the world cried when they were depressed, but that they only attempted suicide in certain cultures."
That might, he says, suggest that the environment has little to do with the physiological expression of affect, but is crucial for understanding and preventing suicide.
Though concrete answers are far off, "Understanding the genes and their interactions will most certainly also help us understand environmental influences," says University of Illinois personality and social psychologist Ed Diener, PhD. "We will be able to see when the environment 'overrides' the genes and why. And we will be able to see how environmental variations interact with genetic variations."
Here&rsquos what a good objective for a resume should contain:
- Strong trait: &ldquoHighly motivated.&rdquo
- Your job title: &ldquocustomer service representative.&rdquo
- 2&ndash3 skills: &ldquotrained in conflict resolution and communication.&rdquo
- Position to which you&rsquore applying: &ldquoseeking to join XYZ as a customer service rep.&rdquo
- An offer: &ldquoto build customer loyalty by leveraging interpersonal skills and offering top customer service.&rdquo
Do you have any questions about how to write great resume objective statements and start landing more interviews? Leave a comment. I&rsquoll be happy to help.
Genetics is an important and commonly taught subject in introductory biology courses at both the college and the high school level. Mendelian genetics continues to be among the most commonly taught concepts (Smith & Gericke, 2015), yet students often have misconceptions about inheritance (Mills Shaw et al., 2008). For these reasons, we chose to use Mendelian genetics as context for a unit plan we designed to explicitly teach students about the nature of science (NOS).
Science educators regularly identify NOS as a priority for student learning. The importance of NOS is based on its being a critical component of science literacy (DeBoer, 1991 Rudge et al., 2014). Although there is no single definition of NOS, there are several agreed-upon concepts. For example, students should be aware of the tentative nature of scientific knowledge, and that scientific knowledge is socially and culturally embedded (Lederman, 2007). Current emphasis on NOS is reflected by its inclusion in the Next Generation Science Standards (NGSS) (NGSS Lead States, 2013). Despite the clear importance of NOS, students and teachers have consistently been shown to have inaccurate views of NOS concepts (Lederman, 2007). One of many proposed strategies for improving understanding is to take advantage of the history of science (Matthews, 1994) to provide context for learning about NOS and to legitimize students’ ideas about science (Monk & Osborne, 1997).
The three-day unit plan presented here explicitly teaches students about two aspects of NOS: (1) the impact of scientists’ backgrounds and (2) that change is an enduring feature of science. The unit plan teaches students about these NOS aspects through the context of Gregor Mendel’s classic research on pea plants, specifically its social and cultural dimensions. Students attempt to confirm Mendel’s classic ratios using simulation software to conduct their own investigations. In the process, they learn about alternative inheritance patterns to Mendelian genetics, and gain insight into the nature of science as a process.
The following examples of research objectives based on several published studies on various topics demonstrate how the research objectives are written:
- This study aims to find out if there is a difference in quiz scores between students exposed to direct instruction and flipped classrooms (Webb and Doman, 2016).
- This study seeks to examine the extent, range, and method of coral reef rehabilitation projects in five shallow reef areas adjacent to popular tourist destinations in the Philippines (Yeemin et al., 2006).
- This study aims to investigate species richness of mammal communities in five protected areas over the past 20 years (Evans et al., 2006).
- This study aims to clarify the demographic, epidemiological, clinical, and radiological features of 2019-nCoV patients with other causes of pneumonia (Zhao et al., 2020).
- This research aims to assess species extinction risks for sample regions that cover some 20% of the Earth’s terrestrial surface.
Finally, writing the research objectives requires constant practice, experience, and knowledge about the topic investigated. Clearly written objectives save time, money, and effort.
Once you have a clear idea of your research objectives, you can now develop your conceptual framework which is a crucial element of your research paper as it guides the flow of your research. The conceptual framework will help you develop your methodology and statistical tests.
I wrote a detailed, step-by-step guide on how to develop a conceptual framework with illustration in my post titled “Conceptual Framework: A Step by Step Guide on How to Make One.“
The dominant alleles TR are on different chromosomes
** The dominant alleles TR are on the same chromosome
Using the pipe cleaners and beads, one partner constructs the male genotype, and the other constructs the female genotype.
Have your teacher check your chromosomes before proceeding! Instructor Initials
If you got this step wrong, your simulation data will be wrong.
Make Your Predictions
With the parents genotypes given. Predict the phenotypic ratios for each trait, using a punnet square method. Show your work and calculations below
Run the Simulation
As parents, you will contribute HALF of your genes to the offspring. For each chromosome set, place behind your back and have your partner choose at random which chromosome will be donated to the offspring. One chromosome of each type must be donated from each parent, so that the offspring has a complete set. The offspring will probably not be like either of the parents.
You will repeat this process 12 times to complete the data table below. For each offspring, list the phenotype for each trait.
1. Compare your predicted values with your actual values (from the simulation).
|Black Females||Spotted Females||Black Males||White Males||Type AB Blood||Type A Blood||Type B Blood||Normal tail, round ears||Normal Tail, pointed ears||Bobtail, round ears||Bobtail, pointed ears|
|Actual (from simulation)|
|Predicted (from Punnett)|
2. What would you expect the phenotypic ratios to be if the alleles for tail length and ear shape were NOT located on the same chromosome?