<p class="jove_content">The authors have nothing to disclose.</p>

predicting the effectiveness of population replacement strategy using mathematical modeling

<p class="jove_content">Charles Taylor e John Marshall explicar a utilidade de modelos matemáticos para avaliar a eficácia da estratégia de reposição da população. Insight é dada em como modelos computacionais podem fornecer informações sobre a dinâmica populacional de mosquitos ea propagação de elementos transponíveis através da subespécie A. gambiae. As considerações éticas de liberar os mosquitos geneticamente modificados na natureza são discutidos.</p>

Watch this Scientific Journal Video about Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling at JoVE.com

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Prever a eficácia da estratégia de substituição População Usando Modelagem Matemática

Charles Taylor and John Marshall explain the utility of mathematical modeling for evaluating the effectiveness of population replacement strategy.  ...

predicting-effectiveness-population-replacement-strategy-using

Research

JoVE Journal

Biology

8.7K visualizações.  University of California, Los Angeles. Charles Taylor e John Marshall explicar a utilidade de modelos matemáticos para avaliar a eficácia da estratégia de reposição da população. Insight é dada em como modelos computacionais podem fornecer informações sobre a dinâmica populacional de mosquitos ea propagação de elementos transponíveis através da subespécie A. gambiae. As considerações éticas de liberar os mosquitos geneticamente modificados na natureza são discutidos.

Vídeo: Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Population Replacement Strategies for Controlling Vector Populations and the Use of Wolbachia pipientis for Genetic Drive

In this video, Jason Rasgon discusses population replacement strategies to control vector-borne diseases such as malaria and dengue. "Population replacement" is the replacement of wild vector populations (that are competent to transmit pathogens) with those that are not competent to transmit pathogens. There are several theoretical strategies to accomplish this. One is to exploit the maternally-inherited symbiotic bacteria Wolbachia pipientis. Wolbachia is a widespread reproductive parasite that spreads in a selfish manner at the extent of its host's fitness. Jason Rasgon discusses, in detail, the basic biology of this bacterial symbiont and various ways to use it for control of vector-borne diseases.

<p class="jove_content">In this interview, Jason Rasgon explains the concept of genetic drive and the characteristics of an effective gene drive system.  The use of the endosymbiotic bacterium, Wolbachia pipientis, as a means to spread genes through mosquito populations, is hypothesized.</p>

População Estratégias de substituição para Populações Vector Controle e Uso de Wolbachia para pipientis Unidade Genética

In this video, Jason Rasgon discusses population replacement strategies to control vector-borne diseases such as malaria and dengue. "Population ...

Biologia

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

<p class="jove_content">Brood parasites lay their eggs in other females' nests, leaving the host parents to hatch and rear their young. Studying how brood parasites manipulate hosts into raising their young and how hosts detect parasitism provide important insights in the field of coevolutionary biology. Brood parasites, such as cuckoos and cowbirds, gain an evolutionary advantage because they do not have to pay the costs of rearing their own young. However, these costs select for host defenses against all developmental stages of parasites, including eggs, their young, and adults. Egg rejection experiments are the most common method used to study host defenses. During these experiments, a researcher places an experimental egg in a host nest and monitors how hosts respond. Color is often manipulated, and the expectation is that the likelihood of egg discrimination and the degree of dissimilarity between the host and experimental egg are positively related. This paper serves as a guide for conducting egg rejection experiments from describing methods for creating consistent egg colors to analyzing the findings of such experiments. Special attention is given to a new method involving uniquely colored eggs along color gradients that has the potential to explore color biases in host recognition. Without standardization, it is not possible to compare findings between studies in a meaningful way; a standard protocol within this field will allow for increasingly accurate and comparable results for further experiments.</p>

<p class="jove_content">This protocol provides guidelines for running egg rejection experiments: outlining techniques for painting experimental egg models to emulate the colors of natural bird eggs, conducting fieldwork, and analyzing the collected data. This protocol provides a uniform method for conducting comparable egg rejection experiments.</p>

Sondar os limites do reconhecimento de ovo usando ovo experiências de rejeição ao longo de gradientes fenotípicas

Brood parasites lay their eggs in other females' nests, leaving the host parents to hatch and rear their young. Studying how brood parasites manipulate ...

Comportamento

Forecasting Hepatocellular Carcinoma Mortality using a Weighted Regression Model to Estimate Cohort Effects in Taiwan

<p class="jove_content">To eliminate the influence of age and period in age cycle contingency table data, a multistage method was adopted to evaluate the cohort effect. The most general primary malignant tumor of the liver is hepatocellular carcinoma (HCC). HCC is associated with liver cirrhosis with alcohol and viral etiologies. In epidemiology, long-term trends in HCC mortality were delineated (or forecasted) by using an age-period-cohort (APC) model. The HCC deaths were determined for each cohort with its weighted influence. The confidence interval (CI) of the weighted mean is fairly narrow (compared to the equally weighted estimates). Due to the fairly narrow CI with less uncertainty, the weighted mean estimation was used as a means for forecasting. With the multistage method, it is recommended to use weighted mean estimation based on a regression model to evaluate the cohort effect in the age-period contingency table data.</p>

<p class="jove_content">We depict a multistage method to measure a cohort effect with age data, thereby allowing data to be eliminated in many situations without sacrificing data quality. The protocol demonstrates the strategy and provides a weighted regression model for analyzing the hepatocellular carcinoma data.</p>

To eliminate the influence of age and period in age cycle contingency table data, a multistage method was adopted to evaluate the cohort effect. The most ...

Pesquisa do câncer

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

<p class="jove_content">Finding the cost-efficient (<em>i.e.</em>, lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (<em>e.g.</em>,<sup>5,12,20</sup>) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization.</p>
<p class="jove_content">Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods<sup>3,4,9,10,13-15,17-19,22,23,25</sup>. In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model<sup>7</sup> with a multiobjective evolutionary algorithm SPEA2<sup>26</sup>, and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.</p>

<p class="jove_content">This work demonstrates an integration of a water quality model with an optimization component utilizing evolutionary algorithms to solve for optimal (lowest-cost) placement of agricultural conservation practices for a specified set of water quality improvement objectives. The solutions are generated using a multi-objective approach, allowing for explicit quantification of tradeoffs.</p>

Otimização Multiobjetivo espacial das práticas agrícolas de conservação, utilizando um modelo SWAT e um algoritmo evolutivo

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals ...

Population Growth

<h4>Population Models</h4>
<p>An ecological population is a group of individuals of a single species living in an area at the same time. To persist, a population must either grow or maintain its size. Population ecology is the study of how population size and age distribution change over time through interactions with other species and the environment as well as with individuals of their own species. A particularly important feature that is studied by ecologists is the rate at which a population grows or declines. A population grows when its birth rate is greater than its death rate. That is, more offspring are being produced than individuals are dying. If the opposite is true and the death rate is greater than the birth rate, the population is in decline and if it continues to decrease it will eventually go extinct.</p>
<p>Population ecologists build models to predict how the size of populations will change over time. Two simple models of population growth are exponential (geometric) growth models and logistic growth models. Exponential population models are density-independent, meaning that per capita birth rates and death rates will remain constant regardless of how many individuals are in the population, therefore these populations increase to infinity. However, logistic growth models are density-dependent, therefore the growth rate of the population is dependent on how many individuals are currently in the population. Geometric or exponential growth can only occur when resources are readily available and in unlimited supply. For example, under optimal growing conditions, bacteria follow an exponential growth pattern, with each organism producing two additional reproducing organisms in the next generation<sup>1</sup>.</p>
<h4>Exponential Growth</h4>
<p>For a population that follows an exponential growth pattern, population ecologists can estimate the number of individuals in the next generation based on the size of the initial population and the growth rate, which represents the proportion of new offspring produced per individual for each generation. With a growth rate of 0.05, 5% of individuals produce one offspring in the next generation. At this rate it would only take 23 generations to produce over a million offspring from 2 initial offspring under exponential growth. Some bacteria can grow and divide very rapidly, and thus would be able to reach large population numbers relatively quickly. For instance, <em>Escherichia coli</em> can double its population in approximately 20 minutes, whereas the rate for <em>Syntrophobacter fumaroxidans</em> is 140 hours<sup>2</sup>.</p>
<p>Geometric or exponential growth cannot go on forever, as eventually resources like food or space will be exhausted, and organisms begin to die more quickly or slow reproduction. The maximum number of individuals in a population that can be supported based on the resources in the environment is known as the carrying capacity (<em>K</em>). When more individuals exist in an area than the area can support, the carrying capacity is said to be exceeded. Natural populations may exceed carrying capacity only briefly, before the lack of resources leads to increased death rates and/or decreased reproduction.</p>
<h4>Logistic Growth</h4>
<p>Populations of many species follow a logistic growth pattern, where the growth rate of the population is dependent on its current size. Therefore, the growth rate of the logistic growth is variable as opposed to the constant growth rate of the exponential model. As the population nears its carrying capacity, its growth begins to decrease, until the number of individuals reaches or briefly exceeds carrying capacity and subsequently maintains a size near carrying capacity. By incorporating a carrying capacity that represents the limiting resources of a population, logistic models consider a more realistic relationship between an organism and its environment.</p>
<h4>Population Dynamics</h4>
<p>Sophisticated models of population dynamics include variables representing real-world interactions between distinct populations. One such example which represents two interacting populations is the predator-prey model that was independently developed by mathematicians Alfred J. Lotka and Vito Volterra in the 1920s. In fact, predator-prey models have been adapted for a variety of species interactions and continue to be used in adapted forms almost a century after they were developed. One population is a prey species, like hares, and the other is a predator, like wolves, that feed on the prey. The species are closely interacting, because the wolf requires the hare as a food source, thus their birth rate is dependent on the amount of prey they consume and how effectively they convert this food into producing new offspring. The prey populations are dependent on the predator population, because their death rate is dependent on the number of prey that are eaten by predators in a generation. The close interaction of the species can result in oscillating patterns shown between their populations.</p>
<p>Models of population dynamics have a variety of applications in conservation biology. Altering models to predict the extinction risk of species is particularly important for helping to protect endangered species, since the loss of one species can result in losses to other species in the food web. The extinction of predator species worldwide is of great concern and can result in instability and extinctions of prey species. Sophisticated modeling and conservation planning will help to protect species in a changing world.</p>
<p>The predator-prey models have also been adapted to study competitive interactions, in which multiple species are competing with each other for resources. These models can be used to predict which species will &#8220;win&#8221; competition interactions and have been applied to the spread of non-native species to predict if a species will be able to establish in a new area, and whether they are likely to become invasive. In most natural applications, models must consider many resources that are available to species, and a complex web of interactions between organisms. For example, lionfish which are native to the Indo-Pacific are invasive in the Atlantic and the Caribbean. Lionfish can reproduce all year long, have no natural predators, and can consume numerous native fish species at unsustainable rates which permanently alters the Atlantic and Caribbean ecosystems. Using population growth models has enabled scientists to determine that approximately a quarter of the entire lionfish population has to be removed from the ecosystem every month just to stunt population growth<sup>3</sup>.</p>
<p>Population growth models have been adapted to fit human populations using demography, which is the study of birth rates, death rates, and statistics related to population sizes based on age groupings. The growth rate of countries and other political districts can be forecast using demographic data, and this is important for planning infrastructure in localities. Thus, application of population growth models is important to planning for the growth of the human population. Further, Lotka-Volterra model has also been applied to study and predict the competition of businesses, further demonstrating the versatility of the population growth models to fields outside of biology<sup>4</sup>.</p>
<h4>References</h4>
<ol class="references">
	<li><strong>Ghislain Y. Gangwe Nana, Camille Ripoll, Armelle Cabin-Flaman, David Gibouin, Anthony Delaune, Laurent Janniere, Gerard Grancher, Gaelle Chagny, Corinne Loutelier-Bourhis, Esther Lentzen, Patrick Grysan, Jean-Nicolas Audinot, Vic Norris.</strong> Division-Based, Growth Rate Diversity in Bacteria. <em>Front Microbiol. </em>2018, Vol. 9, 849 (doi: 10.3389/fmicb.2018.00849).</li>
	<li><strong>Beth Gibson, Daniel J. Wilson, Edward Feil, Adam Eyre-Walker.</strong> The distribution of bacterial doubling times in the wild. <em>Proc Biol Sci. . </em>2018, Vol. 285, 1880 (20180789).</li>
	<li><strong>Rice, James A. MorrisJr.Email authorKyle W. ShertzerJames A.</strong> A stage-based matrix population model of invasive lionfish with implications for control. <em>Biological Invasions. </em>2011, Vol. 13, 1 (7-12).</li>
	<li><strong>Ofer Malcai, Ofer Biham, Peter Richmond, and Sorin Solomon.</strong> Theoretical analysis and simulations of the generalized Lotka-Volterra model. <em>Phys. Rev. E. </em>2002, Vol. 66, 3.</li>
</ol>

Population Growth: Exponential and Logistic Growth and Predator-Prey Model - Concept

Crescimento demográfico

Population Models
An ecological population is a group of individuals of a single species living in an area at the same time. To persist, a population must ...

Education

JoVE Lab Manual

Ecologia

Group Behavior

<h4>Life in a Population</h4>
<p>Organisms in populations interact with one another in complex ways, where individuals compete for resources such as food, shelter, and mates. These interactions are costly since individuals invest energy to acquire the resource, therefore there are a variety of strategies that organisms adopt to gain an advantage over their competitors. This is observed clearly with polymorphic mating systems, in which one sex, predominantly males, display multiple mating strategies. For example, dominant male sea lions defend harems of females up on the beach, whereas non-dominant males try to increase their chances of mating by staying in or near the water, where they mate with females that have left the beach temporarily <sup>1</sup>. Other types of strategies may determine how often an individual will fight for a resource, or how willing they are to cooperate with others.</p>
<h4>Game Theory and Evolutionarily Stable Strategies</h4>
<p>In order to understand how different behavioral strategies evolve, ecologists turn to the game theory, which is a mathematical modeling approach that investigates the outcomes of multi-individual interactions, in which the payoff for any one individual depends its own strategy as well as the strategies of the others <sup>2</sup>. In this approach, the relative cost of the interaction and the benefits obtained from the resource determines the net gain or, in some cases, loss incurred by the organism. Different strategies can be assigned costs and benefits based on those faced by an organism. For example, fighting for control of a resource may provide a large benefit, but it also comes with costs that reduce an organism&#8217;s net gain. On the other hand, a strategy to avoid conflict will reap fewer benefits but incurs no costs.</p>
<p>The organisms with the best interaction strategy maximize their net gain, which in turn will contribute to their fitness. Therefore, over the course of evolutionary time, one strategy may arise that outcompetes all others in a population. This is called an evolutionary stable strategy (ESS) <sup>2</sup>. Populations evolve to adopt this strategy once it arises through mutation or is introduced through migration. Therefore, these strategies are mostly genetic or adopted at a young age, and changes in the strategies used by a population over time are determined by the action of natural selection. This concept is often illustrated using the Hawk-Dove game, which compares the success of two strategies for obtaining resources <sup>3</sup>. In this example, &#8220;hawks&#8221; are aggressive, and always fight for resources. &#8220;Doves,&#8221; on the other hand, are passive, and never fight for resources. In an interaction between two doves, resources are split evenly. When a hawk and a dove interact, the hawk always wins and obtains all of the resources. However, when two hawks interact, they split resources evenly and suffer the cost of their conflict as well <sup>3</sup>. Assessing these interactions over consecutive interactions allows modeling of how competing strategies in an evolving population fare against each other, thus emergence of ESS in an experimental setting.</p>
<p>As seen with the doves in the Hawk-Dove example, organisms not only compete against each other, but also display cooperative behaviors. The risk of being a population of 100% doves is the arrival of a cheater, or an individual that does not cooperate <sup>4</sup>. Cheaters can invade and outcompete the residents, therefore many cooperating populations have strategies in place to prevent invasion, including the ability to switch strategy as necessary or to identify and cheaters, and in some cases, to communicate this information to others in their group to decrease the chances of cheater success <sup>4</sup>.</p>
<h4>Social Interaction in Nature</h4>
<p>The existence of altruism, or a reduction in an organism&#8217;s immediate fitness in order to benefit another&#8217;s, in wild populations has been held up as a counterpoint to the theory of natural selection, however game theory shows how altruism can evolve under certain conditions. Assuming organisms can identify one another or reasonably expect to interact again in the future, acts of apparent altruism may actually be beneficial over time, as an organism can count on the favor being returned. This is seen when flocks of birds or herds of mammals feed collectively &#8211; one individual may sound an alarm call when they spot a predator, making them more vulnerable to attack <sup>5</sup>. The net benefit, however, of others frequently doing the same makes this act adaptive. Similarly, a vampire bat may regurgitate its food to feed hungry individuals. When it is unable to find food in future, it may benefit from the same behavior of another vampire bat <sup>6</sup>.</p>
<p>In addition to the interactions within a single species, the evolution of social interactions can take place between species as well. Besides predator-prey interactions, different species can compete for the same resources and develop strategies to gain advantage over others. However, individuals of a species may also interact cooperatively with members of another species. Interspecies interaction that require cooperation include mutualisms, or situations in which two organisms provide mutual benefits to one another. Many plants form mutualisms with nitrogen-fixing bacteria in the soil, whereby the plants provide complex sugars to bacteria in exchange for nitrogen <sup>7</sup>. Should bacteria fail to provide nitrogen, then plants can reduce the amount of sugars available.</p>
<p>Since cooperative interactions depend on either being able to identify or retaliate against cheaters, it is possible for invaders to take advantage of existing mutualisms in new environments. Invasive species have the potential to be closely related enough to local species that they are able to form mutualisms with other local species, but distantly related enough that existing recognition or defense mechanisms are ineffective. In Hawaii, an invasive dinoflagellate that forms a mutualism with coral takes more resources than local dinoflagellates. This has a negative impact on coral instead of the expected beneficial one <sup>8</sup>. Therefore, studying intra- and interspecies interactions not only allows understanding the development of behavioral strategies in evolving populations but is also essential to assess the behavioral phenotypes of invasive organisms to develop effective strategies against them.</p>
<h4>References</h4>
<ol>
	<li><strong>P&#246;rschmann, UlrichUniBi, et al.</strong> Male reproductive success and its behavioural correlates in a polygynous mammal, the Gal&#225;pagos sea lion (Zalophus wollebaeki). <em>Molecular Ecology. </em>2010, Vol. 19, 12 (2574-86).</li>
	<li><strong>Price, J. M Smith &#38; G. R.</strong> The Logic of Animal Conflict. <em>Nature. </em>1973, 246 (15-18).</li>
	<li><strong>Sigmund K, Nowak MA.</strong> Evolutionary game theory. <em>Curr Biology. </em>1999, Vol. 9, 14 (R503-5).</li>
	<li><strong>Rose, M. R.</strong> Cheating in Evolutionary Games. <em>J. Theor. Biol. </em>1978, 75 (21-34).</li>
	<li><strong>Trivers, R. L.</strong> Evolution of reciprocal altruism. <em>Q. Rev. Biol. </em>1971, Vol. 46, 1 (35-57).</li>
	<li><strong>Wilkinson, G. S.</strong> Reciprocal food sharing in the vampire bat. . <em>Nature. </em>1984, 308(181-84).</li>
	<li><strong>Francis M. Martin, St&#233;phane Uroz, David G. Barker.</strong> Ancestral alliances: Plant mutualistic symbioses with fungi and bacteria. <em>Science. </em>2017, Vol. 356, 6340 (eaad4501).</li>
	<li><strong>Tracy D.Ainsworth, Rebecca VegaThurber, Ruth D.Gates.</strong> The future of coral reefs: a microbial perspective. <em>Trends in Ecology &#38; Evolution. </em>2010, Vol. 25, 4 (233-40).</li>
</ol>

Group Behavior: Hawk-Dove Game and Trading Exercise - Concept

Comportamento do grupo

Life in a Population
Organisms in populations interact with one another in complex ways, where individuals compete for resources such as food, shelter, ...

Hardy-Weinberg and Genetic Drift

<ol class="note-items">
	<li data-note-item="1" data-parent-collapse="">Mathematical Modeling<button class="expand">Expand</button>
	<ul class="lab-items">
		<li data-sub-note-item="1-1">
		<div class="lab-step">Before beginning the mathematical modeling exercise make sure each computer has access to an either on or offline spreadsheet software program.</div>
		</li>
	</ul>
	</li>
	<li data-note-item="2" data-parent-collapse="">Simulation for Hardy-Weinberg and Genetic Drift<button class="expand">Expand</button>
	<ul class="lab-items">
		<li data-sub-note-item="2-1">
		<div class="lab-step">Prepare one set of beads for each pair of students by counting out 50 of one color and then 50 of a second color, for a total of 100 beads.</div>
		</li>
		<li data-sub-note-item="2-2">
		<div class="lab-step">Then, place the beads in a paper bag.</div>
		</li>
		<li data-sub-note-item="2-3">
		<div class="lab-step">Students will also need extra beads to adjust the population makeup and these can be provided in a joint container for the whole class or instructors can make up additional sets for each student pair.</div>
		</li>
		<li data-sub-note-item="2-4">
		<div class="lab-step">Next, create slips of paper and write on these the different experimental scenarios that will test violations of the Hardy-Weinberg Equilibrium.</div>
		</li>
		<li data-sub-note-item="2-5">
		<div class="lab-step">Then, create a second set of paper slips to determine which genetic drift scenario the students will test.</div>
		</li>
		<li data-sub-note-item="2-6">
		<div class="lab-step">Finally, set out any additional beads needed for the experimental scenarios listed on the strips.</div>
		</li>
		<li data-sub-note-item="2-7">
		<div class="lab-step">NOTE: An additional 50 beads in a third color will be needed for the mutation violation, and an extra set of 100 beads in the first two colors for the gene flow scenario. For simulating the founder effect with 10 alleles and natural disaster with 10 alleles scenarios, randomly select 100 beads from 10 different colors.</div>
		</li>
	</ul>
	</li>
</ol>

Hardy-Weinberg Equilibrium Principle and Genetic Drift - Prep

Hardy-Weinberg e a deriva genética


	Mathematical ModelingExpand
	
		
		Before beginning the mathematical modeling exercise make sure each computer has access to an either on or offline ...

Evolução

Lab Manual Sub Articles

Natural Selection

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Simulating Natural Selection
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">To begin, cut the white, black, and gray pipe cleaners into quarters, which should produce a total of 100 pieces that are 3 inches long.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Then, cut the green pipe cleaners in the quantities and lengths specified in the video, taking care to minimize waste. This approximates a normal distribution of lengths and should result in an additional 100 pieces total. NOTE: One 12-inch pipe cleaner should yield multiple lengths, for example, a 5-inch piece and a 7-inch piece. Use this advantage to reduce waste.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Now, cut one sheet of black and one sheet of white construction paper into approximately 3-inch strips measured along the long edge of the paper.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Tape these strips down to cover the base of a dissecting tray to create an alternating pattern of black and white strips. NOTE: In the first simulation, you will use the white, gray, and black pipe cleaner segments against a black and white background to simulate different selection pressures due to predation on a population. You will select your prey using two different strategies, eyes-open and eyes-closed. For each of these strategies, you will perform four rounds consisting of 25 selections, alternating between two partners. After each round you will restore the population back to its original size using the proportions of phenotypes present after selection.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">To begin the first scenario, count out 12 white, 12 black, and 26 gray pipe cleaner segments with your partner. Hypotheses: Here the experimental hypothesis might be that different predation strategies, such as having the eyes open or closed, will result in distinct selection pressures on the populations. Further, different phenotypes may be positively selected in the alternate scenarios. The null hypothesis might be that different predation strategies will have no effect on the populations or phenotypes.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">Mix the pipe cleaner segments together as best as possible. Then, sprinkle them into the papered tray. NOTE: Ideally, the pipe cleaners will be spread out. However, this may not occur due to their self-adhering nature.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Once the segments are in the tray, separate them as best as possible without moving them too far from where they landed. As few segments as possible should be touching.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">Have the first partner close their eyes. When given an audible signal by the second partner, the first should open their eyes and grab the first pipe cleaner they see from the tray as quickly as possible.
</div></li><li data-sub-note-item="1-9"><div class="lab-step">Set the removed pipe cleaner to the side.
</div></li><li data-sub-note-item="1-10"><div class="lab-step">Alternating partners, repeat this process a total of 25 times (steps 8 &#8211; 9).
</div></li><li data-sub-note-item="1-11"><div class="lab-step">Count the remaining pipe cleaners by color and record this in the data collection table for Scenario 1. <a href="https://www.jove.com/CDNSource/lm/labs/23/Lab_23_Tables_1_and_2.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Tables 1 and 2</a>
</div></li><li data-sub-note-item="1-12"><div class="lab-step">Then, restore the population to 50 by adding one pipe cleaner of the same color for each of the remaining pipe cleaners. Hold these to the side until they are all gathered.
</div></li><li data-sub-note-item="1-13"><div class="lab-step">Then, sprinkle the pipe cleaners into the tray as previously described (steps 6-7). Repeat this process for three more rounds consisting of 25 selections each and record the counts for each of the rounds in the data collection table for Scenario 1.
</div></li><li data-sub-note-item="1-14"><div class="lab-step">Remove all pipe cleaners from the tray after the fourth round.
</div></li><li data-sub-note-item="1-15"><div class="lab-step">For Scenario 2, again count out 12 white, 12 black, and 26 gray pipe cleaner segments and sprinkle them randomly into the tray.
</div></li><li data-sub-note-item="1-16"><div class="lab-step">Follow the same protocol as in Scenario 1. However, when given the signal, the grabbing partner should keep their eyes closed. To aid in accuracy, it may help to position the hand near to the tray before grabbing.
</div></li><li data-sub-note-item="1-17"><div class="lab-step">Repeat this process 25 times, switching partner roles, and record the remaining pipe cleaner amounts in the data collection table for Scenario 2.
</div></li><li data-sub-note-item="1-18"><div class="lab-step">Then, restore the population to 50 based on that count. Do this for all four rounds. NOTE: In the second simulation, you will be using the eyes-open and eyes-closed predation strategies as previously described to select pipe cleaners of various lengths.
</div></li><li data-sub-note-item="1-19"><div class="lab-step">For Scenario 3, gather all 100 of the pipe cleaners that have been cut to varying lengths to represent different phenotypes.
</div></li><li data-sub-note-item="1-20"><div class="lab-step">One partner should hold all the pipe cleaners upright and loosely in one hand resting on the tabletop, such that the bottom end of each pipe cleaner rests against the tabletop.
</div></li><li data-sub-note-item="1-21"><div class="lab-step">Now, the partner that is not holding the pipe cleaners should close their eyes. When given an audible signal, the partner should open their eyes and grab the first pipe cleaner they see from their partner&#39;s hand.
</div></li><li data-sub-note-item="1-22"><div class="lab-step">Repeat this process 50 times (step 21). However, the partners should not switch roles during the round this time. The partner holding the pipe cleaners should always be the one giving the signal and never grabbing from the bunch.
</div></li><li data-sub-note-item="1-23"><div class="lab-step">After the first round consisting of 50 selections, count the remaining pipe cleaners by length. Then, record this information in the data collection table for scenario three. <a href="https://www.jove.com/CDNSource/lm/labs/23/Lab_23_Tables_3_and_4.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Tables 3 and 4</a>
</div></li><li data-sub-note-item="1-24"><div class="lab-step">Add one pipe cleaner of the same length as each of the remaining ones to restore the population to 100. This will likely require more pipe cleaners than were originally cut. Try to use scraps from the class to reduce waste.
</div></li><li data-sub-note-item="1-25"><div class="lab-step">Repeat this for three more rounds, alternating which partner holds or grabs each round.
</div></li><li data-sub-note-item="1-26"><div class="lab-step">To perform Scenario 4, gather the varied-length pipe cleaners in their original proportions.
</div></li><li data-sub-note-item="1-27"><div class="lab-step">Now repeat the grabbing process, as described in Scenario 3. However, only remove 30 pipe cleaners instead of 50.
</div></li><li data-sub-note-item="1-28"><div class="lab-step">Divide the count at the end of each round by two for each phenotype 4 inches or below, rounding fractions down.
</div></li><li data-sub-note-item="1-29"><div class="lab-step">Then, record these values in the data recording table for Scenario 4.
</div></li><li data-sub-note-item="1-30"><div class="lab-step">Use the relative proportions of the phenotypes in the final population to restore the population to 100. When restoring the population to 100, it may be necessary to calculate relative proportions of phenotypes.
</div></li><li data-sub-note-item="1-31"><div class="lab-step">Repeat this for three more rounds, again alternating holding and grabbing the pipe cleaners.
</div></li><li data-sub-note-item="1-32"><div class="lab-step">For each scenario calculate the survival for each phenotype, that is the proportion of the phenotype surviving.
</div></li><li data-sub-note-item="1-33"><div class="lab-step">Then, calculate the relative fitness, or w, between each round. As reproductive rate does not vary in this simulation, this should always be considered equal, or 1.
</div></li><li data-sub-note-item="1-34"><div class="lab-step">Also, calculate the selection coefficients for each phenotype.
</div></li><li data-sub-note-item="1-35"><div class="lab-step">Now, use the relative frequencies of each phenotype to create histograms with appropriately labeled axes for each scenario showing the population before any selection took place and then alongside these in a different color after four rounds of selection.
</div></li><li data-sub-note-item="1-36"><div class="lab-step">Identify the type of selection taking place in each scenario and label the graphs. 
</div></li><li data-sub-note-item="1-37"><div class="lab-step">Develop a hypothesis to explain your observations. 
</div></li></ul></li></ol>

Natural Selection: Calculating Fitness and Selection - Procedure

Seleção natural

Simulating Natural Selection
ExpandTo begin, cut the white, black, and gray pipe cleaners into quarters, which should produce a total of 100 pieces that ...

Conservation of Declining Populations

<p>Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent populations from going extinct.</p>

<p>Conservation efforts often utilize scientific approaches to identify the reasons, or the agents, causing the population to decline. This approach then devises steps to remove, oppose, or neutralize the agents.</p>

<p>Conservation efforts may also introduce a test group to determine the probable cause of the decline. The translocation of the remaining population to unaffected areas follows if the cause is confirmed. In cases where the remaining population is too low, or at risk of further reduction, a protected stock is bred rapidly and released to bolster the population. Subsequent monitoring confirms the success of population re-establishment.</p>

<p>The conservation approach has successfully restored the population of an endangered Australian bird&mdash;the Lord Howe Woodhen. These flightless birds are endemic to Lord Howe Island off the Australian coast. The population of these birds began dwindling since the beginning of human inhabitation within the island. The population had reached a state of extinction when an ornithologist, Dr. Ben Miller, identified the situation.</p>

<p>Methodical testing and rejection of several hypotheses identified the feral pigs as responsible for the contraction of the population size of these birds. These pigs, introduced into the island with human settlement, killed and ate the incubating birds and also destroyed their nests and eggs.</p>

<p>Miller&rsquo;s finding resulted in the elimination of the pigs and instituting a breeding program to rear these endangered birds within an enclosed facility. The progeny from the breeding program was released into the wild in batches and monitored closely. Subsequently, the released birds started breeding, and stabilizing their population and saturating all the suitable habitat on the island.</p>

Conservation of Declining Populations in an Ecosystem

Conservação de Populações em Declínio

Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent ...

JoVE Core

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TERMOS E CONCEITOS CHAVE

Mechanistic Models: Compartment Models in Individual and Population Analysis

<p>Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (<em>X</em><sub>i</sub>), observed concentrations (<em>C</em><sub>i</sub>), measurement errors (<em>&#949;</em><sub>i</sub>), model parameters (<em>&#981;</em><sub>j</sub>), and the related function (<em>&#402;</em><sub>i</sub>) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS) method favors better predictions for larger observations. In contrast, weighted least squares (WLS) and maximum likelihood/expected least squares (ML/ELS) methods improve OLS by incorporating a weighting factor.</p>
<p>Population analysis models predict concentration data for multiple individuals, accounting for interindividual variability and providing individual and population predictions. The same structural model fits all individuals&#39; data for a specific drug under study. Different types of population compartmental analysis include na&#239;ve-average data, na&#239;ve pooled data, and the two-stage approach, which includes standard, global, and iterative types. In the two-stage approach, population parameter estimates are obtained through iterative processes, such as standard two-stage (STS) and global two-stage (GTS).</p>

Modelos Mecanísticos: Modelos Compartimentais em Análise Individual e Populacional

The individual analysis uses mechanistic models involving single-source data. However, data collection errors prevent perfect observed data prediction.
In ...