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18 - Current velocity shapes the functional connectivity of benthiscapes to stream insect movement

from Part IV - Methodological issues in the use of simple feedforward networks

Published online by Cambridge University Press:  05 July 2011

By
Julian D. Olden
Edited by
Colin R. Tosh  and
Graeme D. Ruxton
Julian D. Olden
Affiliation:
University of Washington
Colin R. Tosh
Affiliation:
University of Leeds
Graeme D. Ruxton
Affiliation:
University of Glasgow
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Summary

18.1 Introduction

Ecological thresholds have long intrigued scientists, dating from the study of threshold effects for age-specific human mortality (Gompertz, 1825) to present-day investigations for biodiversity conservation and environmental management (Roe & van Eeten, 2001; Folke et al., 2005; Huggett, 2005; Groffman et al., 2006). Defined as a sudden change from one ecological condition to another, ecological thresholds are considered synonymous with discontinuities in any property of a system that occurs in nonlinear response to smooth and continuous change in an independent variable. Understanding ecological thresholds and incorporating them into ecological and socio-ecological systems is seen as a major advance in our ability to forecast and thus properly cope with environmental change (Carpenter, 2002; Rial et al., 2004; Gordon et al., 2008). Consequently, ecologists and economists continue to be extremely attracted to the idea that ecological thresholds may exist and can be used in a management context (Muradian, 2001).

Empirical studies of ecological thresholds are diverse and have grown in number in recent years (Walker & Meyers, 2004). In landscape ecology, a working hypothesis is the existence of critical threshold levels of habitat loss and fragmentation that result in sudden reductions in species' occupancy (Gardner et al., 1987; Andrén, 1994; With & Crist, 1995). As the landscape becomes dissected into smaller and smaller parcels, landscape connectivity – referring to the spatial contagion of habitat – may suddenly become disrupted (With & Crist, 1995).

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Modelling Perception with Artificial Neural Networks , pp. 351 - 373
Publisher: Cambridge University Press
Print publication year: 2010

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