A new optimal foraging model predicts habitat use by drift-feeding stream minnows

– There is substantial need for models that accurately predict habitat selection by fishes for purposes ranging from the elaboration of ecological theory to the preservation of biodiversity. We have developed a new and highly tractable optimal foraging model for drift‐feeding fishes that is based on...

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Published inEcology of freshwater fish Vol. 11; no. 1; pp. 2 - 10
Main Authors Grossman, G. D., Rincon, P. A., Farr, M. D., Ratajczak Jr, R. E.
Format Journal Article
LanguageEnglish
Published Oxford, UK Blackwell Science, Ltd 01.03.2002
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Abstract – There is substantial need for models that accurately predict habitat selection by fishes for purposes ranging from the elaboration of ecological theory to the preservation of biodiversity. We have developed a new and highly tractable optimal foraging model for drift‐feeding fishes that is based on the profitability of occupying varying focal‐point velocities in a stream. The basic model can be written as: Ix = (Ex * Px) = {(D * A * V) * [1/(1 + e(b + cV))]} − Sx, where: (1) Ix is the net energy intake at velocity x; (2) E is prey encounter rate; (3) P is prey capture success rate which can be modelled as 1/(1 + e(b + cV)) where b and c are fitting constants from the prey capture success curve; (4) D is the energy content of prey (J/m3) in the drift; (5) A is the visual reactive area of the fish; (6) V is velocity (cm/s); and (7) S is the cost of maintaining position (J/s). Given that D, A and S can be considered constant over the range of velocities occupied by these fishes, the model reduces to e(b + cV) = 1/(cV − 1) which we solved iteratively to yield an optimal focal‐point velocity for species in each sample. We tested the model by comparing its predictions to the mean focal‐point velocities (i.e. microhabitats) occupied by four species of drift‐feeding minnows in two sites in a stream in North Carolina, USA. The model successfully predicted focal‐point velocities occupied by these species (11 out of 14 cases) in three seasonal samples collected over 2 years at two sites. The unsuccessful predictions still were within 2 cm/s of the 95% confidence intervals of mean velocities occupied by fishes, whereas the overall mean deviation between optimal velocities and mean fish velocities was small (range = 0.9 and 3.3 cm/s for the warpaint shiner and the Tennessee shiner, respectively). Available focal‐point velocities ranged from 0–76 to 0–128 cm/s depending on site and season. Our findings represent one of the more rigorous field tests of an optimal foraging/habitat selection model for aquatic organisms because they encompass multiple species and years, and for one species, multiple sites. Because of the ease of parameterization of our model, it should be readily testable in a range of lotic habitats. If validated in other systems, the model should provide critical habitat information that will aid in the management of riverine systems and improve the performance of a variety of currently used management models (e.g. instream flow incremental methodology (IFIM) and total maximum daily load calculations (TMDL)). Resumen 1. Existe una grave necesidad de modelos que predigan con precisión la selección de hábitat por parte de los peces con fines que van del desarrollo de la teoría ecológica a la conservación de la biodiversidad. Nosotros hemos desarrollado un modelo nuevo y de fácil manejo de alimentación óptima para peces que se alimentan de la deriva que se fundamenta en los diferentes beneficios energéticos derivados de ocupar velocidades focales distintas en un río. 2. El modelo básico puede formularse como: Ix = (Ex * Px) = {(D * A * V) * [1/(1 + e(b + cV))]} − Sx, donde: (1) Ix es el energía neta obtenida a la velocidad, x; (2) V es la velocidad (cm/s); (3) A es el area visual de reacción del pez; (4) D es la energía contenida en las presas (J/m3) en la deriva; (5) E es la tasa de encuentro de presas; (6) P es la probabilidad de captura de la presa, que puede representarse como 1/(1 + e(b + cV)) donde b y c son constantes; y (7) S es el coste de nadar para mantener la posición en la corriente (J/s). Puesto que D, A y S pueden considerarse constantes en el rango de velocidades que ocupan estos peces, el modelo se reduce a e(b + cV) = 1/(cV − 1) que resolvimos iterativamente para obtener una velocidad focal óptima para cada especie en cada muestreo. 3. Probamos el modelo comparando su predicciones con la velocidades focales medias (i.e. microhabitats) ocupadas por cuatro especies de ciprínidos que se alimentan de la deriva en un río de Carolina del Norte. El modelo predijo con éxito las velocidades focales ocupadas por estas especies (11/14 casos) en tres muestreos estacionales llevados a cabo a lo largo de dos años en dos estaciones. Incluso las predicciones fallidas se diferenciaron en menos de 2 cm/s del límite de confianza al 95% CIs de las velocidades medias ocupadas, y la diferencia media entre predicciones y observaciones fue pequeña (rango = 0.9 cm/s warpaint shiner, a 3.3‐cm/s Tennessee shiner). El rango de las velocidades focales medias disponibles fue de 0–76 cm/s a 0–128 cm/s dependiendo de la localidad y estación del año. 4. Nuestros resultados son una de las pruebas de campo más rigurosas de un modelo de alimentación óptima/selección de hábitat para organismos acuáticos puesto que incluyen diversas especies, años y, para una de las especies, localidades. La facilidad de la estima de los parámetros del modelo hace que sea fácil probarlo en diversos hábitats lóticos. Si es validado en ellos, el modelo debería proporcionar información valiosa que ayudará a la gestión de los sistemas fluviales y mejorará los resultados obtenidos a través de varios modelos usados actualmente para la gestión (p.e. IFIM y cálculos TMDL).
AbstractList Abstract – There is substantial need for models that accurately predict habitat selection by fishes for purposes ranging from the elaboration of ecological theory to the preservation of biodiversity. We have developed a new and highly tractable optimal foraging model for drift‐feeding fishes that is based on the profitability of occupying varying focal‐point velocities in a stream. The basic model can be written as: I x  = ( E x  *  P x ) = {( D  *  A  *  V ) * [1/(1 +  e ( b + cV ) )]} −  S x , where: (1) I x is the net energy intake at velocity x ; (2) E is prey encounter rate; (3) P is prey capture success rate which can be modelled as 1/(1 +  e ( b + cV ) ) where b and c are fitting constants from the prey capture success curve; (4) D is the energy content of prey (J/m 3 ) in the drift; (5) A is the visual reactive area of the fish; (6) V is velocity (cm/s); and (7) S is the cost of maintaining position (J/s). Given that D , A and S can be considered constant over the range of velocities occupied by these fishes, the model reduces to e ( b + cV )  = 1/( cV  − 1) which we solved iteratively to yield an optimal focal‐point velocity for species in each sample. We tested the model by comparing its predictions to the mean focal‐point velocities (i.e. microhabitats) occupied by four species of drift‐feeding minnows in two sites in a stream in North Carolina, USA. The model successfully predicted focal‐point velocities occupied by these species (11 out of 14 cases) in three seasonal samples collected over 2 years at two sites. The unsuccessful predictions still were within 2 cm/s of the 95% confidence intervals of mean velocities occupied by fishes, whereas the overall mean deviation between optimal velocities and mean fish velocities was small (range = 0.9 and 3.3 cm/s for the warpaint shiner and the Tennessee shiner, respectively). Available focal‐point velocities ranged from 0–76 to 0–128 cm/s depending on site and season. Our findings represent one of the more rigorous field tests of an optimal foraging/habitat selection model for aquatic organisms because they encompass multiple species and years, and for one species, multiple sites. Because of the ease of parameterization of our model, it should be readily testable in a range of lotic habitats. If validated in other systems, the model should provide critical habitat information that will aid in the management of riverine systems and improve the performance of a variety of currently used management models (e.g. instream flow incremental methodology (IFIM) and total maximum daily load calculations (TMDL)). Resumen 1. Existe una grave necesidad de modelos que predigan con precisión la selección de hábitat por parte de los peces con fines que van del desarrollo de la teoría ecológica a la conservación de la biodiversidad. Nosotros hemos desarrollado un modelo nuevo y de fácil manejo de alimentación óptima para peces que se alimentan de la deriva que se fundamenta en los diferentes beneficios energéticos derivados de ocupar velocidades focales distintas en un río. 2. El modelo básico puede formularse como: I x  = ( E x  *  P x ) = {( D  *  A  *  V ) * [1/(1 +  e ( b + cV ) )]} −  S x , donde: (1) I x es el energía neta obtenida a la velocidad, x ; (2) V es la velocidad (cm/s); (3) A es el area visual de reacción del pez; (4) D es la energía contenida en las presas (J/m 3 ) en la deriva; (5) E es la tasa de encuentro de presas; (6) P es la probabilidad de captura de la presa, que puede representarse como 1/(1 +  e ( b + cV ) ) donde b y c son constantes; y (7) S es el coste de nadar para mantener la posición en la corriente (J/s). Puesto que D , A y S pueden considerarse constantes en el rango de velocidades que ocupan estos peces, el modelo se reduce a e ( b + cV )  = 1/( cV  − 1) que resolvimos iterativamente para obtener una velocidad focal óptima para cada especie en cada muestreo. 3. Probamos el modelo comparando su predicciones con la velocidades focales medias (i.e. microhabitats) ocupadas por cuatro especies de ciprínidos que se alimentan de la deriva en un río de Carolina del Norte. El modelo predijo con éxito las velocidades focales ocupadas por estas especies (11/14 casos) en tres muestreos estacionales llevados a cabo a lo largo de dos años en dos estaciones. Incluso las predicciones fallidas se diferenciaron en menos de 2 cm/s del límite de confianza al 95% CIs de las velocidades medias ocupadas, y la diferencia media entre predicciones y observaciones fue pequeña (rango = 0.9 cm/s warpaint shiner, a 3.3‐cm/s Tennessee shiner). El rango de las velocidades focales medias disponibles fue de 0–76 cm/s a 0–128 cm/s dependiendo de la localidad y estación del año. 4. Nuestros resultados son una de las pruebas de campo más rigurosas de un modelo de alimentación óptima/selección de hábitat para organismos acuáticos puesto que incluyen diversas especies, años y, para una de las especies, localidades. La facilidad de la estima de los parámetros del modelo hace que sea fácil probarlo en diversos hábitats lóticos. Si es validado en ellos, el modelo debería proporcionar información valiosa que ayudará a la gestión de los sistemas fluviales y mejorará los resultados obtenidos a través de varios modelos usados actualmente para la gestión (p.e. IFIM y cálculos TMDL).
– There is substantial need for models that accurately predict habitat selection by fishes for purposes ranging from the elaboration of ecological theory to the preservation of biodiversity. We have developed a new and highly tractable optimal foraging model for drift‐feeding fishes that is based on the profitability of occupying varying focal‐point velocities in a stream. The basic model can be written as: Ix = (Ex * Px) = {(D * A * V) * [1/(1 + e(b + cV))]} − Sx, where: (1) Ix is the net energy intake at velocity x; (2) E is prey encounter rate; (3) P is prey capture success rate which can be modelled as 1/(1 + e(b + cV)) where b and c are fitting constants from the prey capture success curve; (4) D is the energy content of prey (J/m3) in the drift; (5) A is the visual reactive area of the fish; (6) V is velocity (cm/s); and (7) S is the cost of maintaining position (J/s). Given that D, A and S can be considered constant over the range of velocities occupied by these fishes, the model reduces to e(b + cV) = 1/(cV − 1) which we solved iteratively to yield an optimal focal‐point velocity for species in each sample. We tested the model by comparing its predictions to the mean focal‐point velocities (i.e. microhabitats) occupied by four species of drift‐feeding minnows in two sites in a stream in North Carolina, USA. The model successfully predicted focal‐point velocities occupied by these species (11 out of 14 cases) in three seasonal samples collected over 2 years at two sites. The unsuccessful predictions still were within 2 cm/s of the 95% confidence intervals of mean velocities occupied by fishes, whereas the overall mean deviation between optimal velocities and mean fish velocities was small (range = 0.9 and 3.3 cm/s for the warpaint shiner and the Tennessee shiner, respectively). Available focal‐point velocities ranged from 0–76 to 0–128 cm/s depending on site and season. Our findings represent one of the more rigorous field tests of an optimal foraging/habitat selection model for aquatic organisms because they encompass multiple species and years, and for one species, multiple sites. Because of the ease of parameterization of our model, it should be readily testable in a range of lotic habitats. If validated in other systems, the model should provide critical habitat information that will aid in the management of riverine systems and improve the performance of a variety of currently used management models (e.g. instream flow incremental methodology (IFIM) and total maximum daily load calculations (TMDL)). Resumen 1. Existe una grave necesidad de modelos que predigan con precisión la selección de hábitat por parte de los peces con fines que van del desarrollo de la teoría ecológica a la conservación de la biodiversidad. Nosotros hemos desarrollado un modelo nuevo y de fácil manejo de alimentación óptima para peces que se alimentan de la deriva que se fundamenta en los diferentes beneficios energéticos derivados de ocupar velocidades focales distintas en un río. 2. El modelo básico puede formularse como: Ix = (Ex * Px) = {(D * A * V) * [1/(1 + e(b + cV))]} − Sx, donde: (1) Ix es el energía neta obtenida a la velocidad, x; (2) V es la velocidad (cm/s); (3) A es el area visual de reacción del pez; (4) D es la energía contenida en las presas (J/m3) en la deriva; (5) E es la tasa de encuentro de presas; (6) P es la probabilidad de captura de la presa, que puede representarse como 1/(1 + e(b + cV)) donde b y c son constantes; y (7) S es el coste de nadar para mantener la posición en la corriente (J/s). Puesto que D, A y S pueden considerarse constantes en el rango de velocidades que ocupan estos peces, el modelo se reduce a e(b + cV) = 1/(cV − 1) que resolvimos iterativamente para obtener una velocidad focal óptima para cada especie en cada muestreo. 3. Probamos el modelo comparando su predicciones con la velocidades focales medias (i.e. microhabitats) ocupadas por cuatro especies de ciprínidos que se alimentan de la deriva en un río de Carolina del Norte. El modelo predijo con éxito las velocidades focales ocupadas por estas especies (11/14 casos) en tres muestreos estacionales llevados a cabo a lo largo de dos años en dos estaciones. Incluso las predicciones fallidas se diferenciaron en menos de 2 cm/s del límite de confianza al 95% CIs de las velocidades medias ocupadas, y la diferencia media entre predicciones y observaciones fue pequeña (rango = 0.9 cm/s warpaint shiner, a 3.3‐cm/s Tennessee shiner). El rango de las velocidades focales medias disponibles fue de 0–76 cm/s a 0–128 cm/s dependiendo de la localidad y estación del año. 4. Nuestros resultados son una de las pruebas de campo más rigurosas de un modelo de alimentación óptima/selección de hábitat para organismos acuáticos puesto que incluyen diversas especies, años y, para una de las especies, localidades. La facilidad de la estima de los parámetros del modelo hace que sea fácil probarlo en diversos hábitats lóticos. Si es validado en ellos, el modelo debería proporcionar información valiosa que ayudará a la gestión de los sistemas fluviales y mejorará los resultados obtenidos a través de varios modelos usados actualmente para la gestión (p.e. IFIM y cálculos TMDL).
There is substantial need for models that accurately predict habitat selection by fishes for purposes ranging from the elaboration of ecological theory to the preservation of biodiversity. We have developed a new and highly tractable optimal foraging model for drift-feeding fishes that is based on the profitability of occupying varying focal-point velocities in a stream. The basic model can be written as: I sub(x) = (E sub(x) * P sub(x)) = {(D * A * V)* [1/(1 + e super(b + cV)))]} - S sub(x), where: (1) I sub(x) is the net energy intake at velocity x; (2) E is prey encounter rate; (3) P is prey capture success rate which can be modelled as 1/(1 + e super((b + cV))) where b and c are fitting constants from the prey capture success curve; (4) D is the energy content of prey (J/m super(3)) in the drift; (5) A is the visual reactive area of the fish; (6) V is velocity (cm/s); and (7) S is the cost of maintaining position (J/s). Given that D, A and S can be considered constant over the range of velocities occupied by these fishes, the model reduces to e super((b + cV)) = 1/(cV - 1) which we solved iteratively to yield an optimal focal-point velocity for species in each sample. We tested the model by comparing its predictions to the mean focal-point velocities (i.e. microhabitats) occupied by four species of drift-feeding minnows in two sites in a stream in North Carolina, USA. The model successfully predicted focal-point velocities occupied by these species (11 out of 14 cases) in three seasonal samples collected over 2 years at two sites. The unsuccessful predictions still were within 2 cm/s of the 95% confidence intervals of mean velocities occupied by fishes, whereas the overall mean deviation between optimal velocities and mean fish velocities was small (range = 0.9 and 3.3 cm/s for the warpaint shiner and the Tennessee shiner, respectively). Available focal-point velocities ranged from 0-76 to 0-128 cm/s depending on site and season.
Author Ratajczak Jr, R. E.
Farr, M. D.
Grossman, G. D.
Rincon, P. A.
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  surname: Grossman
  fullname: Grossman, G. D.
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– sequence: 2
  givenname: P. A.
  surname: Rincon
  fullname: Rincon, P. A.
  organization: Departamento de Ecologia Evolutiva, Museo Nacional de Ciencias Naturales, C.S.I.C, C/. Jose Gutierrez Abascal 2, Madrid, Spain
– sequence: 3
  givenname: M. D.
  surname: Farr
  fullname: Farr, M. D.
  organization: Current address: US Army Corps of Engineers, St Paul, Minnesota, USA
– sequence: 4
  givenname: R. E.
  surname: Ratajczak Jr
  fullname: Ratajczak Jr, R. E.
  organization: D. B. Warnell School of Forest Resources, University of Georgia, Athens, Georgia, USA, and
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Snippet – There is substantial need for models that accurately predict habitat selection by fishes for purposes ranging from the elaboration of ecological theory to...
Abstract – There is substantial need for models that accurately predict habitat selection by fishes for purposes ranging from the elaboration of ecological...
There is substantial need for models that accurately predict habitat selection by fishes for purposes ranging from the elaboration of ecological theory to the...
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SubjectTerms cost-benefit
Cyprinidae
focal-point velocity
foraging success
IFIM
interspecific competition
microhabitat
optimization
Pisces
stream fishes
TMDL
Title A new optimal foraging model predicts habitat use by drift-feeding stream minnows
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