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Cited by 322

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Life-History Strategies for Energy Gain and Predator Avoidance Under Time Constraints

Ludwig, Donald, Rowe, Locke·1990·The American Naturalist

Abstract

Short-term foraging behavior is typically influenced by the needs to obtain food at a high rate and to avoid predation. There is increasing evidence that the need to balance these conflicting demands plays a role in ontogenetic habitat shifts, including the spectacular shifts characteristic of complex life cycles. Previous theory has led to rules that are independent of time to predict the size at which habitat shifts take place. We develop a model that incorporates time constraints, by assuming that reproduction or some other major event, such as diapause or metamorphosis, must occur by a specified time or date. We incorporate recent formulations of dynamic programming that allow strategies to balance conflicting behaviors by expressing them in the common currency of future reproductive output. The resulting theory predicts optimal strategies for pre-reproductive habitat shifts that depend on both time and body weight. Our theory, although derived from a single set of assumptions, leads to a synthesis of insights gained from a diversity of previous dynamic optimization problems.

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Metadata

Authors
Ludwig, D., & Rowe, L.
Venue
The American Naturalist
Published
1990-05
Volume
135
Issue
5
Pages
686-707
ISSN
  • 0003-0147 (ISSN-L)
  • 1537-5323
OpenAlex
W2051458414
Cited by
322
References
0
FWCI
35.72
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Life-History Strategies for Energy Gain and Predator Avoidance Under Time Constraints. https://app.cochat.ai/papers/10.1086/285069
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Ludwig, D., & Rowe, L. (1990). Life-History Strategies for Energy Gain and Predator Avoidance Under Time Constraints. The American Naturalist. https://doi.org/10.1086/285069

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.