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Considerable effort has been devoted to the empirical estimation of species interaction strengths. This effort has focused primarily on statistical significance testing and on obtaining point estimates of parameters that contribute to interaction strength magnitude, leaving characterizations of estimation uncertainty and distinctions between the de...
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... Novak, and Gitelman S3 In our dataset, predator feeding surveys included covariate information (predator size, prey size, and temperature) that was used to estimate field handling times on the basis of regression models for handling times parameterized using laboratory data. In estimating attack rates we treated the field covariates as part of the handling times data and assumed they were independent of the feeding proportions data . The validity of this assumption may be assessed by plotting the regression covariates versus the observed feeding proportions, as shown in figure S1. In this figure, every point represents a single feeding survey. The x-axes are the averages of the (log-tranformed) covariate and the y-axes are the proportions of predators feeding. Only two species had sufficient data to be plotted and showed little evidence of a dependence. If a lack of independence were evident it would need to be accounted for in the covariates distribution model. That is, although our model for the covariates was ...Similar publications
We investigated competition between Salpa thompsoni and protistan grazers during Lagrangian experiments near the Subtropical Front in the southwest Pacific sector of the Southern Ocean. Over a month, the salp community shifted from dominance by large (> 100 mm) oozooids and small (< 20 mm) blastozooids to large (~ 60 mm) blastozooids. Phytoplankton...
Considerable effort has been devoted to the estimation of species interaction strengths. This effort has focused primarily on statistical significance testing and obtaining point estimates of parameters that contribute to interaction strength magnitudes, leaving the characterization of uncertainty associated with those estimates unconsidered. We co...
Considerable effort has been devoted to the empirical estimation of species interaction strengths. This effort has focused primarily on statistical significance testing and on obtaining point estimates of parameters that contribute to interaction strength magnitude, leaving characterizations of estimation uncertainty and distinctions between the de...
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... Such refocusing on parameters rather than per capita interaction strengths per se will clarify distinctions among differing uses of the term interaction strength (cf. Berlow et al. 2004, Wootton & Emmerson 2005 ), will aid in avoiding the strict equilibrium assumptions implicit in many estimation approaches (Novak & Wootton 2010, Wootton & Emmerson 2005), and will permit the field to move beyond current sensitivity analyses to more probabilistic descriptions of interaction strengths and predictions than are currently possible (Petchey et al. 2015, Wolf et al. 2015). Many additional challenges exist to understanding the dynamics of real ecological systems, such that humility and adaptive strategies will always be necessary (Doak et al. 2008, Petchey et al. 2015). ...
The community matrix is among ecology’s most important mathematical abstractions, formally encapsulating the interconnected network of effects that species have on one another’s populations. Despite its importance, the term “community matrix” has been applied to multiple types of matrices that have differing interpretations. This has hindered the application of theory for understanding community structure and perturbation responses. Here we clarify the correspondence and distinctions among the Interaction matrix, the Alpha matrix, and the Jacobian matrix, terms that are frequently used interchangeably as well as synonymously with the term “community matrix.” Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics Volume 47 is November 01, 2016. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Intraspecific variation in ecologically relevant traits is widespread. In generalist predators in particular, individual diet specialization is likely to have important consequences for food webs. Understanding individual diet specialization empirically requires the ability to quantify individual diet preferences accurately. Here we compare the currently used frequentist maximum likelihood approach which infers individual preferences using the observed prey proportions to Bayesian hierarchical models that instead estimate these proportions. Using simulated and empirical data, we find that the approach of using observed prey proportions consistently overestimates diet specialization relative to the Bayesian hierarchical approach when the number of prey observations per individual is low or the number of prey observations vary among individuals, two common features of empirical data. Furthermore, the Bayesian hierarchical approach permits the estimation of point estimates for both prey proportions and their variability within and among levels of organization (i.e. individuals, experimental treatments, populations), while also characterizing the uncertainty of these estimates in ways inaccessible to frequentist methods. The Bayesian hierarchical approach provides a useful framework for improving the quantification and understanding of intraspecific variation in diet specialization studies. This article is protected by copyright. All rights reserved.
We consider the goal of predicting how complex networks respond to chronic (press) perturbations when characterizations of their network topology and interaction strengths are associated with uncertainty. Our primary result is the derivation of exact formulas for the expected number and probability of qualitatively incorrect predictions about a system's responses under uncertainties drawn form arbitrary distributions of error. These formulas obviate the current use of simulations, algorithms, and qualitative modeling techniques. Additional indices provide new tools for identifying which links in a network are most qualitatively and quantitatively sensitive to error, and for determining the volume of errors within which predictions will remain qualitatively determinate (i.e. sign insensitive). Together with recent advances in the empirical characterization of uncertainty in ecological networks, these tools bridge a way towards probabilistic predictions of network dynamics.