Literature DB >> 23565330

The logic of ecological patchiness.

Daniel Grünbaum1.   

Abstract

Most ecological interactions occur in environments that are spatially and temporally heterogeneous-'patchy'-across a wide range of scales. In contrast, most theoretical models of ecological interactions, especially large-scale models applied to societal issues such as climate change, resource management and human health, are based on 'mean field' approaches in which the underlying patchiness of interacting consumers and resources is intentionally averaged out. Mean field ecological models typically have the advantages of tractability, few parameters and clear interpretation; more technically complex spatially explicit models, which resolve ecological patchiness at some (or all relevant) scales, generally lack these advantages. This report presents a heuristic analysis that incorporates important elements of consumer-resource patchiness with minimal technical complexity. The analysis uses scaling arguments to establish conditions under which key mechanisms-movement, reproduction and consumption-strongly affect consumer-resource interactions in patchy environments. By very general arguments, the relative magnitudes of these three mechanisms are quantified by three non-dimensional ecological indices: the Frost, Strathmann and Lessard numbers. Qualitative analysis based on these ecological indices provides a basis for conjectures concerning the expected characteristics of organisms, species interactions and ecosystems in patchy environments.

Entities:  

Keywords:  consumer–resource interactions; ecological model; foraging behaviour; patch dynamics; scaling analysis

Year:  2012        PMID: 23565330      PMCID: PMC3293200          DOI: 10.1098/rsfs.2011.0084

Source DB:  PubMed          Journal:  Interface Focus        ISSN: 2042-8898            Impact factor:   3.906


  7 in total

1.  Catching ghosts with a coarse net: use and abuse of spatial sampling data in detecting synchronization.

Authors:  Natalia Petrovskaya; Sergei Petrovskii
Journal:  J R Soc Interface       Date:  2017-02       Impact factor: 4.118

2.  Towards the marriage of theory and data.

Authors:  Simon A Levin
Journal:  Interface Focus       Date:  2012-02-01       Impact factor: 3.906

3.  Contrasting phytoplankton-zooplankton distributions observed through autonomous platforms, in-situ optical sensors and discrete sampling.

Authors:  Glaucia M Fragoso; Emlyn J Davies; Trygve O Fossum; Jenny E Ullgren; Sanna Majaneva; Nicole Aberle; Martin Ludvigsen; Geir Johnsen
Journal:  PLoS One       Date:  2022-09-06       Impact factor: 3.752

4.  Scaling the risk landscape drives optimal life-history strategies and the evolution of grazing.

Authors:  Uttam Bhat; Christopher P Kempes; Justin D Yeakel
Journal:  Proc Natl Acad Sci U S A       Date:  2019-12-17       Impact factor: 11.205

5.  A predictive model and a field study on heterogeneous slug distribution in arable fields arising from density dependent movement.

Authors:  Sergei Petrovskii; John Ellis; Emily Forbes; Natalia Petrovskaya; Keith F A Walters
Journal:  Sci Rep       Date:  2022-02-10       Impact factor: 4.379

Review 6.  When are bacteria really gazelles? Comparing patchy ecologies with dimensionless numbers.

Authors:  Samuel S Urmy; Alli N Cramer; Tanya L Rogers; Jenna Sullivan-Stack; Marian Schmidt; Simon D Stewart; Celia C Symons
Journal:  Ecol Lett       Date:  2022-03-22       Impact factor: 11.274

7.  Virio- and bacterioplankton microscale distributions at the sediment-water interface.

Authors:  Lisa M Dann; James G Mitchell; Peter G Speck; Kelly Newton; Thomas Jeffries; James Paterson
Journal:  PLoS One       Date:  2014-07-24       Impact factor: 3.240

  7 in total

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