Nonlinear Functional Responses Research Articles

Reply [to “Comments on ‘Determination of nonlinear functional response functions in rainfall-runoff processes’ by J. Amorocho and A. Brandstetter”

Reply [to “Comments on ‘Determination of nonlinear functional response functions in rainfall-runoff processes’ by J. Amorocho and A. Brandstetter”

Comments on ‘Determination of nonlinear functional response functions in rainfall-runoff processes’ by J. Amorocho and A. Brandstetter

Water Resources ResearchVolume 9, Issue 3 p. 764-765 LettersFree Access Comments on ‘Determination of nonlinear functional response functions in rainfall-runoff processes’ by J. Amorocho and A. Brandstetter A. Boneh, A. BonehSearch for more papers by this authorM. H. Diskin, M. H. DiskinSearch for more papers by this author A. Boneh, A. BonehSearch for more papers by this authorM. H. Diskin, M. H. DiskinSearch for more papers by this author First published: June 1973 https://doi.org/10.1029/WR009i003p00764Citations: 1AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. References Amorocho, J., A. Brstetter, Determination of nonlinear functional response functions in rainfall-runoff processes, Water Resour. Res., 7(5), 1087– 1101, 1971. Boneh, A., Identification of the kernel functions of hydrologic systems from independent multistorm records, D.Sc. thesis,Technion-Israel Institute of Technology, Haifa, 1971. Diskin, M. H., A. Boneh, Properties of the kernels for time invariant, initially relaxed, second order hydrologic systems, J. Hydrol., 17(1/2), 115– 141, 1972. Diskin, M. H., A. Boneh, Determination of optimal kernels for second-order stationary surface runoff systems, Water Resour. Res., 9(2), 311– 325, 1973. Mikusinski, J., Operational Calculus, Pergamon, Oxford, 1959. Citing Literature Volume9, Issue3June 1973Pages 764-765 ReferencesRelatedInformation

Determination of Nonlinear Functional Response Functions in Rainfall‐Runoff Processes

The operation of some hydrologie systems can be represented by a functional series expansion involving higher order nonlinear generalizations of the convolution integral, which underlies the unit hydrograph concept. A method was developed to determine the linear and nonlinear response functions of such systems on the basis of concurrent records of the input and output. This procedure was successfully tested in a special laboratory catchment and in a natural watershed to establish rainfall‐runoff relations.

Dyspareunia.

<h3>Abstract</h3> Growth in individual size or biomass is a key demographic component in population models, with wide-ranging applications from quantifying species performance across abiotic or biotic conditions to assessing landscape-level dynamics under global change. In forest ecology, the responses of tree growth to biotic interactions are widely held to be crucial for understanding forest diversity, function, and structure. To date, most studies on plant–plant interactions only examine the additive competitive or facilitative interactions between species pairs; however, there is increasing evidence of non-additive, higher-order interactions (HOIs) impacting species demographic rates. When HOIs are present, the dynamics of a multi-species community cannot be fully understood or accurately predicted solely from pairwise outcomes because of how additional species ‘interfere’ with the direct, pairwise interactions. Such HOIs should be particularly prevalent where species show nonlinear functional responses to resource availability and resource-acquisition traits themselves are density dependent. With this in mind, we used data from a tropical secondary forest—a system that fulfills both of these conditions—to build a ontogenetic diameter-growth model for individuals across ten woody-plant species. We allowed both direct and indirect interactions within communities to influence the species-specific growth parameters in a generalized Lotka–Volterra model. Specifically, indirect interactions entered the model as higher-order quadratic terms, i.e. non-additive effects of conspecific and heterospecific neighbour size on the focal individual’s growth. For the whole community and for four out of ten focal species, the model that included HOIs had more statistical support than the model that included only direct interactions, despite the former containing a far greater number of parameters. HOIs had comparable effect sizes to direct interactions, and tended to further reduce the diameter growth rates of most species beyond what direct interactions had already reduced. In a simulation of successional stand dynamics, the inclusion of HOIs lead to rank swaps in species’ diameter hierarchies, even when community-level size distributions remained qualitatively similar. Our study highlights the implications, and discusses possible mechanisms, of non-additive density dependence in highly diverse and light-competitive tropical forests.

A COMPARISON OF TWO MODELS OF THE FUNCTIONAL RESPONSE WITH EMPHASIS ON PARAMETER ESTIMATION PROCEDURES

AbstractThe two primary functional response models extant in recent entomological literature were compared using a common set of data. Use of an iterative least squares parameter estimation routine was shown to be essential to a comparison of these two models. With parameters properly determined, the Watt model fit data on one out of five experiment days best, the Holling model fit data best on four out of five experiment days. The "handling time" submodel was shown to require iterative least squares parameter estimation rather than the usual log transformation and subsequent fitting of this logistic function by standard linear regression techniques. The conclusion was reached that if accuracy in prediction by nonlinear functional response models is desired, proper methods of determining parameter values are of critical importance.