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Content Provider | World Health Organization (WHO)-Global Index Medicus |
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Author | Watson, James R. Kendall, Bruce E. Siegel, David A. Mitarai, Satoshi |
Description | Country affiliation: United States Author Affiliation: Watson JR ( Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ 08540, USA. jrwatson@princeton.edu) |
Abstract | The probability of dispersal from one habitat patch to another is a key quantity in our efforts to understand and predict the dynamics of natural populations. Unfortunately, an often overlooked property of this potential connectivity is that it may change with time. In the marine realm, transient landscape features, such as mesoscale eddies and alongshore jets, produce potential connectivity that is highly variable in time. We assess the impact of this temporal variability by comparing simulations of nearshore metapopulation dynamics when potential connectivity is constant through time (i.e., when it is deterministic) and when it varies in time (i.e., when it is stochastic). We use mathematical analysis to reach general conclusions and realistic biophysical modeling to determine the actual magnitude of these changes for a specific system: nearshore marine species in the Southern California Bight. We find that in general the temporal variability of potential connectivity affects two important quantities: metapopulation growth rates when the species is rare and equilibrium abundances. Our biophysical models reveal that stochastic outcomes are almost always lower than their deterministic counterparts, sometimes by up to 40%. This has implications for how we use spatial information, such as connectivity, to manage nearshore (and other) systems. |
File Format | HTM / HTML |
ISSN | 00030147 |
Issue Number | 1 |
Volume Number | 180 |
e-ISSN | 15375323 |
Journal | The American Naturalist |
Language | English |
Publisher | The University of Chicago Press |
Publisher Date | 2012-07-01 |
Publisher Place | United States |
Access Restriction | Subscribed |
Subject Keyword | Discipline Ecology Discipline Evolutionary Biology Ecosystem Models, Biological Animals Animals, Newborn Fishes Population Dynamics Stochastic Processes Journal Article Research Support, U.s. Gov't, Non-p.h.s. |
Content Type | Text |
Resource Type | Article |
Subject Domain (in MeSH) | Eukaryota Investigative Techniques Biological Phenomena Social Sciences |
Subject | Ecology, Evolution, Behavior and Systematics |
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