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

K12 Standards

Next Generation Science Standards

High School

Life Science

heredity-inheritance-and-variation-of-traits

Heredity: Inheritance and Variation of Traits

HS-LS4-2

Optimal Foraging

A menudo en la vida, las cosas que un individuo necesita pueden no estar al alcance inmediato. Por lo tanto, los organismos necesitan alimentarse. La búsqueda de alimento se define simplemente como el proceso de búsqueda de alimentos o, en general, de recursos. Aunque la comida es probablemente el primer ejemplo que viene a la mente, los organismos deben buscar otros recursos como refugio o parejas, y los animales a menudo hacen todo lo posible para obtener lo que necesitan. Sin embargo, el acto de buscar comida en sí mismo requiere energía. Esencialmente, las personas deben equilibrar la quema de calorías con la obtención de ellas. En pocas palabras, la búsqueda de alimento requiere una estrategia. Los ecologistas crearon la teoría del forrajeo óptimo para modelar las circunstancias en las que un organismo cumple con el equilibrio óptimo entre la energía gastada en el forrajeo, es decir, el valor de dos bellotas y la energía potencial que puede adquirir como seis bellotas nuevas. 
Una característica de la búsqueda de alimento es que los recursos no siempre se distribuyen de manera uniforme dentro de un hábitat. Por lo general, los recursos están contenidos en parches entre los que los recolectores deben moverse. Entonces, ¿cómo deberían navegar exactamente los recolectores por estos parches de recursos? En 1976, el ecólogo evolutivo Eric L. Charnov ideó el teorema del valor marginal o MVT para explicar cómo los organismos optimizan su búsqueda de alimento. El MVT tiene cinco predicciones principales para un forrajeo óptimo. En primer lugar, establece que los recolectores deben capturar más presas en parches con alta densidad de presas que en parches de baja densidad de presas. En segundo lugar, la teoría predice que los recolectores deberían pasar más tiempo buscando recursos en parches de alta densidad de presas. A continuación, el recolector debería tener una tasa de captura de presas más alta en entornos densos con parches muy apretados que en entornos dispersos con parches más separados. La cuarta predicción establece que los recolectores pasarán más tiempo buscando alimento en estos ambientes densos que en ambientes dispersos. Finalmente, un recolector debe dejar un parche cuando la tasa de captura haya disminuido a la tasa promedio de todos los parches. Y aquí es donde entra en juego un concepto llamado renunciar al tiempo o GUT. Para calcular el GUT, simplemente resta el momento en que se adquirió la presa final del momento en que el individuo abandona el parche. Por lo tanto, el GUT debe ser el mismo para todos los parches que visita un recolector. 
En este laboratorio, completará simulaciones de búsqueda de alimento en varios parches de recursos artificiales diferentes y luego realizará cálculos para medir cómo se desempeñó su clase como recolectores y si se adhirió a las reglas del teorema del valor marginal><.

forrajeo óptimo

<h4>Forrajeo óptimo</h4>
Los organismos deben adquirir y utilizar los recursos de su entorno para sobrevivir. Si bien la comida es uno de los principales recursos que los organismos deben buscar, los individuos también necesitan buscar hábitats, refugio y parejas. Este proceso de búsqueda de recursos se conoce como forrajeo, que conlleva una serie de costes y beneficios. Más específicamente, la adquisición de un recurso proporciona un beneficio al organismo, sin embargo, buscar y capturar el recurso requiere gasto de tiempo y energía. Por lo tanto, los organismos adoptan estrategias de búsqueda de alimento que maximizan la ganancia neta a un nivel óptimo al proporcionar el mayor beneficio al menor costo. Los ecólogos desarrollaron la "Teoría de la Búsqueda Óptima de Alimento" para modelar las situaciones en las que un organismo alcanza el equilibrio óptimo entre los costos y beneficios de la búsqueda de alimento.
<h4>El teorema del valor marginal</h4>
Los recursos no se distribuyen uniformemente en el entorno. Por ejemplo, en un ecosistema forestal, es mucho más probable encontrar especies de árboles agrupadas en lugar de distribuidas uniformemente por todo el bosque. Esta heterogeneidad en la disponibilidad de recursos crea "parches" de recursos. Por lo tanto, los recolectores deben considerar tanto la recompensa de un parche como la distancia entre parches, por lo tanto, el costo de pasar a otro parche. El Teorema del Valor Marginal (MVT) describe cómo los recolectores óptimos utilizan y se mueven entre parches de recursos en su hábitat 1. Hay cinco predicciones principales del MVT: 
<ol class="referencias">
	<li>Los recolectores deben capturar más presas en parches de alta densidad de presas que en parches de baja densidad de presas.</li>
	<li>Los recolectores deben pasar más tiempo buscando alimento en parches de alta densidad de presas que en aquellos con baja densidad de presas.</li>
	<li>Los recolectores deberían tener una tasa de captura de presas más alta en parches densos que aquellos que están más separados.</li>
	<li>Los recolectores deben pasar más tiempo buscando en entornos densos, donde los parches están agrupados, que en entornos dispersos.</li>
	<li>Los recolectores deben dejar un parche cuando la tasa de captura haya disminuido a la tasa promedio de todos los parches.</li>
</ol>
El intervalo de tiempo entre el momento en que se captura la última presa y el momento en que el recolector abandona el parche se conoce como el Tiempo de Entrega, o GUT. En el contexto del MVT, el GUT debe ser el mismo en todos los parches para cada recolector individual. Esto se probó en un experimento con carboneros de cabeza negra en un gran aviario que buscaba gusano de la harina escondido en piñas artificiales, donde las aves tenían el mismo GUT para todos los tipos de parches, lo que se relacionó inversamente con la tasa de captura promedio para todo el ambiente 2. 
Cuanto más tiempo pasa un organismo buscando alimento, más energía gasta y más tiempo se expone a los depredadores y a los elementos, por lo tanto, el desarrollo de una estrategia óptima de búsqueda de alimento para la adquisición de recursos es esencial para la mayoría de los organismos. Los principios del MVT tienen muchas aplicaciones en la vida real para promover la diversidad, la conservación e incluso el comportamiento humano.
<h4>Forrajeo óptimo en un entorno cambiante</h4>
Desde el punto de vista de la diversidad y la conservación, el MVT predeciría que las especies solo pueden existir si hay parches que satisfagan adecuadamente las necesidades energéticas de un organismo, por lo que la disponibilidad y la distancia entre los recursos es importante para los organismos que dependen de ellos para sobrevivir. La pérdida de hábitat, la contaminación y otros cambios en el medio ambiente pueden interferir con la capacidad de un organismo para alimentarse de manera óptima, lo que reduce su aptitud. Así, cuando se destruyen los hábitats de un ecosistema, se eliminan parches o comunidades enteras. Esta eliminación limita los recursos disponibles para los habitantes del resto de un ecosistema. Menos parches con recursos crea una competencia más intensa y puede eliminar ciertas especies. Esto es particularmente importante para las especies en peligro de extinción debido a la pérdida de hábitat, que no podrán satisfacer sus necesidades de alimentación si se destruye la mayor parte de su hábitat. 
Los seres humanos han desarrollado un sofisticado almacenamiento de alimentos para limitar las necesidades de búsqueda de alimento, sin embargo, hay muchos otros casos en los que es necesario buscar comida, como buscar una vivienda asequible, un trabajo bien remunerado o encontrar una fecha adecuada, entre otros. Un estudio reciente puso a prueba las predicciones de la MVT utilizando las habilidades de búsqueda de información de los médicos generales. Descubrieron que los médicos generales buscaban información de manera óptima al diagnosticar a un paciente; Se movieron rápidamente entre múltiples fuentes de información y utilizaron tanto fuentes de alta densidad, como Internet, como fuentes fácilmente alcanzables, incluyendo a sus colegas con frecuencia 3. De manera similar, la Teoría del Forrajeo Óptimo también se aplica a la economía en la forma en que los consumidores compran, o "buscan" productos e información al realizar una compra 4. Estos estudios demuestran cómo los seres humanos se adhieren a las predicciones de la teoría del forrajeo óptimo en una variedad de contextos.
<h4>Referencias</h4>
<ol>
	<li>Charnov, E. L. Forrajeo ptimal: el teorema del valor marginal. Biología Teórica de Poblaciones. 1976, Vol. 9, (129-36).</li>
	<li>John Krebs, John Ryan, Eric Charnov. Caza por expectativa o forrajeo óptimo: Un estudio del uso de parches por parte de los carboneros. comportamiento animal. 1974, Vol. 22, (953-64).</li>
	<li>Mai Dwairy, Anthony C Dowell, Jean-Claude Stahl. La aplicación de la teoría del forrajeo al comportamiento de búsqueda de información de los médicos generales. BMC Medicina Familiar. 2011, Vol. 12, 90.</li>
	<li>Pozos, V.K. Forrajeo: ¿un modelo ecológico de comportamiento del consumidor? Teoría del Marketing. 2012, Vol. 2, 12 (117-136).</li>
</ol>

Forrajeo óptimo

Forrajeo óptimo
Los organismos deben adquirir y utilizar los recursos de su entorno para sobrevivir. Si bien los alimentos son uno de los principales recursos, los organismos deben ...

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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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Patch Use in Optimal Foraging

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