Name: patch use in optimal foraging
Uploaded: 2019-01-29T11:04:46.000Z
Duration: 3 min 37 s
Channel: JoVE

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Heredity: Inheritance and Variation of Traits

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Optimal Foraging

Oft sind die Dinge, die ein Mensch braucht, im Leben nicht sofort greifbar. Die Organismen müssen also auf Nahrungssuche gehen. Die Nahrungssuche ist einfach definiert als der Prozess der Suche nach Nahrung oder allgemeiner nach Ressourcen. Obwohl Nahrung wahrscheinlich das erste Beispiel ist, das einem in den Sinn kommt, müssen Organismen nach anderen Ressourcen wie Unterschlupf oder Partnern suchen, und Tiere unternehmen oft große Anstrengungen, um das zu bekommen, was sie brauchen. Der Akt der Nahrungssuche selbst erfordert jedoch Energie. Im Wesentlichen müssen Einzelpersonen das Verbrennen von Kalorien mit der Gewinnung von Kalorien in Einklang bringen. Um es einfach auszudrücken: Die Nahrungssuche erfordert eine Strategie. Ökologen haben die Theorie der optimalen Nahrungssuche entwickelt, um die Umstände zu modellieren, unter denen ein Organismus das optimale Gleichgewicht zwischen dem Energieaufwand für die Nahrungssuche, d.h. dem Wert von zwei Eicheln, und der potenziellen Energie, die er wie sechs neue Eicheln erwerben kann, erreicht. 
Ein Merkmal der Nahrungssuche ist, dass die Ressourcen nicht immer gleichmäßig innerhalb eines Lebensraums verteilt sind. In der Regel sind Ressourcen in Patches enthalten, zwischen denen Sammler wechseln müssen. Wie genau sollten sich Sammler also in diesen Ressourcenbereichen zurechtfinden? Im Jahr 1976 entwickelte der Evolutionsökologe Eric L. Charnov das Marginalwerttheorem (MVT), um zu erklären, wie Organismen ihre Nahrungssuche optimieren. Der MVT hat fünf Hauptvorhersagen für eine optimale Nahrungssuche. Zunächst heißt es, dass Sammler in Flecken mit hoher Beutedichte mehr Beute fangen sollten als in Gebieten mit niedriger Beutedichte. Zweitens sagt die Theorie voraus, dass Sammler mehr Zeit mit der Jagd nach Ressourcen in Gebieten mit hoher Beutedichte verbringen sollten. Als nächstes sollte der Sammler in dichten Umgebungen mit dicht beieinander liegenden Flecken eine höhere Beutefangrate aufweisen als in dünnen Umgebungen mit weiter auseinander liegenden Flecken. Die vierte Vorhersage besagt, dass Sammler in diesen dichten Umgebungen mehr Zeit mit der Nahrungssuche verbringen werden als in dünn besiedelten Umgebungen. Schließlich sollte ein Forager einen Patch hinterlassen, wenn die Fangrate auf die durchschnittliche Rate aller Patches gesunken ist. Und hier kommt ein Konzept ins Spiel, das sich "Giving up Time" oder "GUT" nennt. Um den GUT zu berechnen, subtrahieren Sie einfach die Zeit, in der das letzte Beutetier erworben wurde, von der Zeit, in der das Individuum das Beet verlässt. Daher sollte der GUT für alle Flecken, die ein Sammler besucht, gleich sein. 
In diesem Labor führst du Simulationen zur Nahrungssuche in verschiedenen Feldern für künstliche Ressourcen durch und führst dann Berechnungen durch, um zu beurteilen, wie deine Klasse als Sammler abgeschnitten hat und ob du dich an die Regeln des Grenzwertsatzes gehalten hast.

optimale nahrungssuche

<h4>Optimale Nahrungssuche</h4>
Organismen müssen Ressourcen in ihrer Umwelt erwerben und nutzen, um überleben zu können. Während Nahrung eine der primären Ressourcen ist, nach denen Organismen suchen müssen, müssen Individuen auch Lebensräume, Schutz und Partner suchen. Dieser Prozess der Suche nach Ressourcen wird als Sammeln bezeichnet und ist mit einer Reihe von Kosten und Vorteilen verbunden. Genauer gesagt bietet der Erwerb einer Ressource dem Organismus einen Nutzen, aber die Suche und das Fangen der Ressource erfordert den Aufwand von Zeit und Energie. Daher wenden Organismen Futtersuchstrategien an, die den Nettogewinn auf ein optimales Niveau maximieren, indem sie den größten Nutzen zu den geringsten Kosten bieten. Ökologen entwickelten die "Optimal Foraging Theory", um die Situationen zu modellieren, in denen ein Organismus das optimale Gleichgewicht zwischen Kosten und Nutzen der Nahrungssuche erreicht.
<h4>Der Grenzwertsatz</h4>
Ressourcen sind in der Umwelt nicht gleichmäßig verteilt. In einem Waldökosystem ist es zum Beispiel viel wahrscheinlicher, dass Baumarten zusammengeklumpt sind, anstatt gleichmäßig im Wald verteilt zu sein. Diese Heterogenität in der Ressourcenverfügbarkeit führt zu "Patches" von Ressourcen. Daher müssen Forager sowohl den Gewinn eines Patches als auch die Entfernung zwischen den Patches berücksichtigen, also die Kosten für den Wechsel zu einem anderen Patch. Das Marginalwerttheorem (MVT) beschreibt, wie optimale Sammler Ressourcen in ihrem Lebensraum nutzen und zwischen ihnen wechseln 1. Es gibt fünf Hauptvorhersagen des MVT: 
<ol class="Referenzen">
	<li>Sammler sollten in Gebieten mit hoher Beutedichte mehr Beute fangen als in Gebieten mit geringer Beutedichte.</li>
	<li>Sammler sollten in Gebieten mit hoher Beutedichte mehr Zeit mit der Nahrungssuche verbringen als in solchen mit geringer Beutedichte.</li>
	<li>Sammler sollten in dichten Flecken eine höhere Beutefangrate haben als in solchen, die weiter voneinander entfernt sind.</li>
	<li>Sammler sollten mehr Zeit mit der Nahrungssuche in dichten Umgebungen verbringen, in denen die Patches gehäuft sind, als in dünn besiedelten Umgebungen.</li>
	<li>Sammler sollten einen Patch verlassen, wenn die Fangrate auf die durchschnittliche Rate aller Patches gesunken ist.</li>
</ol>
Das Zeitintervall zwischen dem Fang der letzten Beute und dem Zeitpunkt, an dem der Sammler das Feld verlässt, wird als Giving-Up-Time oder GUT bezeichnet. Im Rahmen der MVT sollte der GUT in allen Patches für jeden einzelnen Häcksler gleich sein. Dies wurde in einem Experiment mit Schwarzkappenmeisen in einer großen Voliere getestet, die nach Mehlwürmern suchten, die in künstlichen Tannenzapfen versteckt waren, wobei die Vögel für alle Arten von Flecken den gleichen DARM hatten, was umgekehrt proportional zur durchschnittlichen Fangrate für die gesamte Umgebung war 2. 
Je länger ein Organismus mit der Nahrungssuche verbringt, desto mehr Energie verbraucht er und je länger er sich Räubern und Elementen aussetzt, daher ist die Entwicklung einer optimalen Futtersuchstrategie für den Ressourcenerwerb für die meisten Organismen unerlässlich. Die Prinzipien des MVT haben viele reale Anwendungen zur Förderung von Vielfalt, Naturschutz und sogar menschlichem Verhalten.
<h4>Optimale Nahrungssuche in einer sich verändernden Umgebung</h4>
Unter dem Gesichtspunkt der Vielfalt und des Naturschutzes würde der MVT vorhersagen, dass Arten nur existieren können, wenn es Flächen gibt, die den Energiebedarf eines Organismus angemessen decken, daher ist die Verfügbarkeit und der Abstand zwischen Ressourcen wichtig für Organismen, die zum Überleben auf sie angewiesen sind. Der Verlust von Lebensräumen, Umweltverschmutzung und andere Veränderungen in der Umwelt können die Fähigkeit eines Organismus beeinträchtigen, optimal nach Nahrung zu suchen, und somit seine Fitness verringern. Wenn also Lebensräume in einem Ökosystem zerstört werden, werden Flecken oder ganze Lebensgemeinschaften entfernt. Diese Entfernung schränkt die Ressourcen ein, die den Bewohnern des restlichen Ökosystems zur Verfügung stehen. Weniger Flecken mit Ressourcen führen zu einem intensiveren Wettbewerb und können bestimmte Arten vernichten. Dies ist besonders wichtig für Arten, die aufgrund des Verlusts ihres Lebensraums vom Aussterben bedroht sind und nicht in der Lage sein werden, ihren Nahrungsbedarf zu decken, wenn der größte Teil ihres Lebensraums zerstört wird. 
Der Mensch hat eine ausgeklügelte Lebensmittellagerung entwickelt, um den Bedarf an Nahrungssuche zu begrenzen, aber es gibt viele andere Fälle, in denen die Nahrungssuche notwendig ist, wie z. B. die Suche nach erschwinglichem Wohnraum, einem gut bezahlten Job oder der Suche nach einem geeigneten Datum. In einer kürzlich durchgeführten Studie wurden die Vorhersagen des MVT anhand der informationssuchenden Fähigkeiten von Allgemeinmedizinern getestet. Sie fanden heraus, dass Allgemeinmediziner bei der Diagnose eines Patienten optimal nach Informationen suchten; Sie wechselten schnell zwischen mehreren Informationsquellen und nutzten sowohl Quellen mit hoher Dichte, wie z. B. das Internet, als auch leicht zugängliche Quellen, zu denen häufig auch ihre Kollegen gehörten 3. In ähnlicher Weise lässt sich die Theorie der optimalen Nahrungssuche auch auf die Ökonomie anwenden, wie Verbraucher beim Kauf nach Produkten und Informationen "suchen" 4. Diese Studien zeigen, wie sich der Mensch in einer Vielzahl von Kontexten an die Vorhersagen der Theorie der optimalen Nahrungssuche hält.
<h4>Referenzen</h4>
<ol>
	<li>Charnov, E. L. ptimale Nahrungssuche: der Marginalwertsatz. Theoretische Populationsbiologie. 1976, Bd. 9, (129-36).</li>
	<li>John Krebs, John Ryan, Eric Charnov. Jagd nach Erwartung oder optimaler Nahrungssuche: Eine Studie zur Nutzung von Flächen durch Meisen Tierverhalten. 1974, Bd. 22, (953-64).</li>
	<li>Mai Dwairy, Anthony C Dowell, Jean-Claude Stahl. Die Anwendung der Theorie der Nahrungssuche auf das Informationssuchverhalten von Allgemeinmedizinern. BMC Familienpraxis. 2011, Bd. 12, 90.</li>
	<li>Wells, V.K. Sammeln: ein ökologisches Modell des Konsumverhaltens? Marketing-Theorie. 2012, Bd. 2, 12 (117-136).</li>
</ol>

Optimale Nahrungssuche

Optimale Nahrungssuche
Organismen müssen Ressourcen in ihrer Umwelt erwerben und nutzen, um überleben zu können. Während Nahrung eine der primären Ressourcen ist, müssen Organismen ...

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patch use in optimal foraging

Patch use in optimal foraging explains how organisms balance food gain against the time and energy needed to search for it. Foraging is not limited to food. Animals also search for habitats, shelter, and mates. Each choice has costs and benefits. A resource provides a benefit, but finding and capturing it takes effort. Optimal Foraging Theory models how organisms reach the best balance between those costs and gains.

A key idea in this theory is that resources are often clumped into patches rather than spread evenly through an environment. Forest trees, for example, are more likely to occur in groups than as single, evenly spaced trees. Because of this patchiness, foragers must think about both the value of a patch and the distance to the next one. Moving between patches also has a cost. The Marginal Value Theorem helps explain when an organism should keep feeding in a patch and when it should move on.

The Marginal Value Theorem predicts that foragers should capture more prey in dense patches than in sparse ones. They should also spend more time in patches with more prey, and they should have a higher prey capture rate there. In dense environments, where patches are closer together, foragers should spend more time foraging than in sparse environments. A forager should leave a patch when the capture rate falls to the average rate for the whole environment. The time between the last prey capture and leaving the patch is called the Giving-Up-Time, or GUT.

The GUT should be the same across patches for a single forager. This prediction was tested with black-capped chickadees in a large aviary. The birds searched for mealworms hidden in artificial pine cones. They showed the same GUT in all patch types, and that GUT was inversely related to the average capture rate in the environment. The result supported the Marginal Value Theorem.

Optimal foraging matters because searching takes time, uses energy, and can expose organisms to predators and the elements. It also helps explain real-world ecology and conservation. Habitat loss, pollution, and other environmental changes can disrupt foraging and lower fitness. If habitats are destroyed, patches and even whole communities may disappear. That reduces the resources available to the rest of the ecosystem and can increase competition. Species at risk of extinction may be especially affected if most of their habitat is lost.

The same ideas also apply to human behavior. Humans reduce foraging needs through food storage, but they still forage in other ways. People may search for affordable housing, a well-paying job, or a suitable date. A study of general practitioners found that they foraged for information while diagnosing patients. They moved quickly between sources and used both high-density sources, such as the internet, and easier sources, including colleagues. Optimal Foraging Theory also helps explain how consumers search for products and information when making purchases.

Patch Use in Optimal Foraging

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Patch use in optimal foraging explains how organisms balance food gain against the time and energy needed to search for it. Foraging is not limited to food. Animals also search for habitats, shelter, and mates. Each choice has costs and benefits. A resource provides a benefit, but finding and capturing it takes effort. Optimal Foraging Theory models how organisms reach the best balance between those costs and gains. A key idea in this theory is that resources are often clumped into patches...

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Learn how optimal foraging theory explains patch use, Giving-Up-Time, and the Marginal Value Theorem using chickadees, conservation, and human examples.

Patch Use in Optimal Foraging | JoVE K12

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Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions ...

Make and defend a claim based on evidence that inheritable genetic variations may result from (1) new genetic combinations through meiosis, (2) viable errors occurring during replication, and or (3) mutations caused by environmental factors. [Clarification Statement: Emphasis is on using data to support arguments for the way variation occurs.] [Assessment Boundary: Assessment does not include the phases of meiosis or the biochemical mechanism of specific steps in the process.]

HS-LS3-2

Patch Use in Optimal Foraging

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