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Modeling the animal in its world and the canonical problem for habitat selection
in Israel Journal of Ecology and EvolutionDepartment of Applied Mathematics & Institute of Marine Sciences, University of California Santa Cruz Santa Cruz, CA USA
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Abstract
Behavioral and evolutionary ecology made enormous progress in the last 50 years by using the assumption that the modeler/observer is external to the organism and its environment. This allows specifying details of the environment (e.g. predation risk or the probability of finding food) and then using a fitness optimization model to predict the behaviors that are the end point of natural selection. Doing so can be called the third-person perspective of the organism. More than 80 years ago, Jakob von Uexküll argued that a first-person perspective is possible if we replace the external observerâs description of the environment by the organismâs subjective characterization of itself and its surroundings based on its sensory data, and allow those sensory data to shape behavior. The first-person umwelt model becomes an evolutionary one when the genes determining the sensory responses evolve. I use a canonical problem of habitat selection (which will always be an important problem in biology and which was one of Leon Blausteinâs favorite topics of research) to illustrate construction of first-person umwelt and third-person fitness optimization models. A canonical problem is the simplest but still interesting form of a collection of similar problems, with a focus on what is essential to the collection as a whole. In particular, a canonical problem does not model any particular situation, but has much in common with many situations. Here, the canonical problem focuses on an organism that needs to find a refuge from a harsh environmental season that begins at a fixed time when refuges vary in the level of protection from the harsh environment. Individuals who survive the harsh environmental season successfully reproduce. When searching for refuges, organisms experience predation risk so that the third-person fitness optimization model answers âwhen an organism encounters a habitat of a specific quality at a given time, is it predicted to settle or continue searchingâ? The first-person umwelt model is based on the assumptions that i) the organism has sensory inputs that provide information on quality of a habitat (settling is more likely in higher quality habitats) and the remaining time before the onset of the harsh environmental season (settling is more likely when less time remains), ii) the sensory information is combined to determine behavior, and iii) the genotypic architecture underlying the sensory functions evolves by natural selection. After developing predictions from the models by simulating populations following the rules developed from each, I use effect size measured by Cohenâs d to explore how evolution of the genes of the response functions in the first-person umwelt model affects survival and convergence of the predictions of the first-person umwelt and third-person fitness optimization models.
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Alonzo, S.H., Switzer, P.V., and Mangel, M. (2003). Ecological games in space and time: The distribution and abundance of Antarctic krill and penguins. Ecology 84, pp. 1598â1607.
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Blaustein, L., Kiflawi, M., Eitam, A., Mangel, M., and Cohen, J.E. (2004). Oviposition habitat selection in response to risk of predation in temporary pools: mode of detection and consistency across experimental venue. Oecologia 138, pp. 300â305.
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Budaev, S., Giske, J., and Eliassen, S. (2018). AHA: A general cognitive architecture for Darwinian agents. Biologically Inspired Cognitive Architectures 25, pp. 51â57.
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Budaev, S., Jorgensen, C., Mangel, M., Eliassen, S., and Giske, J. (2019). Decision-making from the animal perspective: Bridging ecology and subjective cognition. Frontiers in Ecology and Evolution 7, Article 164.
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Budaev, S., Dumitru, M.L., Enberg, K., Handeland, S.O., Higgison, A.D., Kristiansen, T.S., Opdal, A.F., Railsback, S.F., Ronnestad, I., Vollset, K.W., Mangel, M., and Giske, J. (2024). Premises for a digital twin of the Atlantic salmon in its world: Agency, robustness, subjectivity and prediction. Aquaculture, Fish, and Fisheries 4, e153. https://doi.org/10.1002/aff2.153.
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Charnov, E.L. and Skinner, S.W. (1985). Complimentary approaches to understanding parasitoid oviposition decisions. Environmental Entomology 14, pp. 383â391.
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Eitam, A., Blaustein, L., and Mangel, M. (2002). Effects of Anisops sardea (Hemiptera: Notonectidae) on oviposition habitat selection by mosquitoes and other dipterans and on community structure in artificial ponds. Hydrobiologia 485, pp. 183â189.
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Eitam, A., Blaustein, L., and Mangel, M. (2005). Density and intercohort effects on larval Salamandra salamandra in temporary pools. Oecologia 146, pp. 36â42.
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Eliassen, S., Andersen, B.S., Jørgensen, C. and Giske, J. (2016). From sensing to emergent adaptations: modelling the proximate architecture for decision-making. Ecological Modelling, 326, pp. 90â100.
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Gardner, S., Gressel, J. and Mangel, M. (1998). A revolving dose strategy to delay the evolution of both quantitative vs major monogene resistances to pesticides and drugs. International Journal of Pest Management 44, 161â180.
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Ginsburg, S. and Jablonka, E. (2019). The Evolution of the Sensitive Soul. Learning and the Origins of Consciousness. Cambridge, MA, USA: MIT Press.
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Giske, J., Eliassen, S., Fiksen, O., Jakobsen, P.J., Aksnes, D.L., Jorgensen, C., and Mangel, M. (2013). Effects of the emotion system on adaptive behavior. The American Naturalist 182, pp. 689â703.
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Giske, J., Eliassen, S., Fiksen, O., Jakobsen, P.J., Aksnes, D.L., Mangel, M. and, Jørgensen, C. (2014). The emotion system promotes diversity and evolvability. Proceedings of the Royal Society B 281, article 20141096.
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Giske, J., Budaev, S., Eliassen, S., Higgison, A.D., Jørgensen, C., and Mangel, M. (2025). Vertebrate decision making leads to the interdependence of behaviour and wellbeing. Animal Behaviour 221, article 123101.
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Giske, J., Dumitru, M.L., Enberg, K., Folkedal, O., Handeland, S.O., Higginson, A.D., Opdal, A.F., Ronnestad, I., Salvanes, A.G.V., Vollset, K.W, Zennaro, F.M., and Mangel, M. (2025a). Premises for digital twins reporting on Atlantic salmon wellbeing. Behavioural Processes 226, article 105163.
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Gomuliewicz, R. and Beder, J. (1996). The selection gradient of an infinite-dimensional trait. SIAM Journal on Applied Mathematics 56, pp. 509â523.
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Gomuliewicz, R., Kingsolver, J.G, Carter, P.A., and Heckman, N. (2018). Variation and evolution of function-valued traits. Annual Review of Ecology, Evolution, and Systematics 17, pp. 139â164.
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Hanlon, R.T. and Messenger, J.B. (1996/2003). Cephalopod Behaviour. Cambridge, UK: Cambridge University Press.
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Hilborn, R. and Mangel, M. (1997). The Ecological Detective. Confronting Models with Data. Princeton, NJ, USA: Princeton University Press.
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Houston, A.I. and McNamara, J.M. (1999). Models of Adaptive Behaviour. An approach based on state. Cambridge, UK: Cambridge University Press.
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Kiflawi, M., Blaustein, L., and Mangel, M. (2003a). Predation-dependent oviposition habitat selection by the mosquoto Culiseta longiareolata: a test of competing hypothesis. Ecology Letters 6, pp. 35â40.
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Kiflawi, M., Blaustein, L., and Mangel, M. (2003b). Oviposition habitat selection by the mosquito Culiseta longiareolata in response to risk of predation and conspecific larval density. Ecological Entomology 28, pp. 168â173.
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Kohl, H.R. and Kohl, J. (2000). A View from the Oak: The Private Worlds of Other Creatures. New York, USA: The New Press.
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Mangel, M. (2015). Oviposition site selection and clutch size in insects. Journal of Mathematical Biology 25, pp. 1â22.
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Mangel, M. and Satterthwaite, W.H. (2008). Combining proximate and ultimate approaches to understand life history variation in salmonids with application to fisheries, conservation, and aquaculture. Bulletin of Marine Science 83, pp. 107â130.
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Marreiros, A.C., Daunizeau, J., Kiebel, S.J. and Friston, K.J. (2008). Population dynamics: variance and the sigmoid activation function. Neuroimage 42(1), pp. 147â157.
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Satterthwaite, W.H., Beakes, M.P., Collins, E., Sawnk, D.R., Merz, J.E., Titus, R.G., Sogard, S.M., and Mangel, M. (2010). State-dependent life history models in a changing (and regulated) environment: steelhead in the California Central Valley. Evolutionary Applications 3, pp. 221â243.
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Saward-Arav, D., Sadeh, A., Mangel, M., Templeton, A.R., and Blaustein, L. (2016). Oviposition responses of two mosquito species to pool size and predator presence: varying trade-offs between desiccation and predation risks. Israel Journal of Ecology and Evolution. https://doi.org/10.1080/15659801.2015.1069113.
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Modeling the animal in its world and the canonical problem for habitat selection
in Israel Journal of Ecology and EvolutionDepartment of Applied Mathematics & Institute of Marine Sciences, University of California Santa Cruz Santa Cruz, CA USA
Search for other papers by Marc Mangel in
Current site
Google Scholar
PubMed
Abstract
Behavioral and evolutionary ecology made enormous progress in the last 50 years by using the assumption that the modeler/observer is external to the organism and its environment. This allows specifying details of the environment (e.g. predation risk or the probability of finding food) and then using a fitness optimization model to predict the behaviors that are the end point of natural selection. Doing so can be called the third-person perspective of the organism. More than 80 years ago, Jakob von Uexküll argued that a first-person perspective is possible if we replace the external observerâs description of the environment by the organismâs subjective characterization of itself and its surroundings based on its sensory data, and allow those sensory data to shape behavior. The first-person umwelt model becomes an evolutionary one when the genes determining the sensory responses evolve. I use a canonical problem of habitat selection (which will always be an important problem in biology and which was one of Leon Blausteinâs favorite topics of research) to illustrate construction of first-person umwelt and third-person fitness optimization models. A canonical problem is the simplest but still interesting form of a collection of similar problems, with a focus on what is essential to the collection as a whole. In particular, a canonical problem does not model any particular situation, but has much in common with many situations. Here, the canonical problem focuses on an organism that needs to find a refuge from a harsh environmental season that begins at a fixed time when refuges vary in the level of protection from the harsh environment. Individuals who survive the harsh environmental season successfully reproduce. When searching for refuges, organisms experience predation risk so that the third-person fitness optimization model answers âwhen an organism encounters a habitat of a specific quality at a given time, is it predicted to settle or continue searchingâ? The first-person umwelt model is based on the assumptions that i) the organism has sensory inputs that provide information on quality of a habitat (settling is more likely in higher quality habitats) and the remaining time before the onset of the harsh environmental season (settling is more likely when less time remains), ii) the sensory information is combined to determine behavior, and iii) the genotypic architecture underlying the sensory functions evolves by natural selection. After developing predictions from the models by simulating populations following the rules developed from each, I use effect size measured by Cohenâs d to explore how evolution of the genes of the response functions in the first-person umwelt model affects survival and convergence of the predictions of the first-person umwelt and third-person fitness optimization models.
| Insgesamt | Letzte 365 Tage | In den letzten 30 Tagen | |
|---|---|---|---|
| Aufrufe von Kurzbeschreibungen | 122 | 122 | 12 |
| Gesamttextansichten | 16 | 16 | 1 |
| PDF-Downloads | 40 | 40 | 3 |
