econullnetr (version 0.2.1)

test_interactions: Compare observed interaction strengths in a network to those estimated from a null model

Description

Takes the result of running a null model with generate_null_net and tests whether the observed interactions between consumer species and resource species differ those expected under the null model.

Usage

test_interactions(nullnet, signif.level = 0.95)

Arguments

nullnet

An object of class "nullnet" from generate_null_net

signif.level

An optional value specifying the threshold used for testing for 'significant' deviations from the null model. Defaults to 0.95

Value

Returns a data frame listing all possible consumer and resource species combinations with the following column headings:

Consumer

The name of the consumer species

Resource

The name of the resource species

Observed

The 'strength' of the observed interaction (e.g. total number of interactions summed across the individual consumers)

Null

The mean strength of the interaction across the iterations of the null model

Lower

Lower confidence limit for the interaction strength

Upper

Upper confidence limit for the interaction strength

Test

Whether the observed interaction is significantly stronger than expected under the null model, weaker or consistent with the null model (ns)

SES

The standardised effect size for the interaction

Details

Statistical significance is determined for each consumer-resource interaction according to whether the observed interaction strength falls outside the confidence limits calculated across the iterations of the null model. Confidence limits are calculated as the 1 -- alpha/2 percentiles from the frequency distribution (Manly 2006).

The observed and expected interactions strengths are also compared by calculating the standardised effect size (Gotelli & McCabe 2002): $$(observed link strength - expected link strength) / standard deviation of the link strength across the iterations of the null model$$

test_interactions will issue warnings when:

1. The number of iterations of the null model was small <100, as the confidence intervals are unlikely to be reliable

2. The number of tests >50, due to the increasing risk of Type I errors (incorrectly denoting an interaction as significantly different from the null model). Many networks will contain many more than 100 potential interactions, so the significance of individual interactions should be treated with caution. Some form of false discovery rate correction may be valuable (e.g. the local false discovery rate; Gotelli & Ulrich 2010).

References

Gotelli, N.J. & McCabe, D.J. (2002) Species co-occurrence: a meta-analysis of J. M. Diamond's assembly rules model. Ecology, 83, 2091--2096.

Gotelli, N.J. & Ulrich, W. (2010) The empirical Bayes approach as a tool to identify non-random species associations. Oecologia, 162, 463--477.

Manly, B.F.J. (2006) Randomization, Bootstrap and Monte Carlo Methods in Biology (3rd edn). Chapman & Hall, Boca Raton.

Vaughan, I.P., Gotelli, N.J., Memmott, J., Pearson, C.E., Woodward, G. & Symondson, W.O.C. (2018) econullnetr: an R package using null models to analyse the structure of ecological networks and identify resource selection. Methods in Ecology and Evolution, 9, 728--733.

See Also

generate_null_net, plot_preferences

Examples

# NOT RUN {
null.1 <- generate_null_net(WelshStreams[, 2:18], WelshStreams.prey[, 2:17],
                            sims = 10, c.samples = WelshStreams[, 1],
                            r.samples = WelshStreams.prey[, 1])
test_interactions(null.1, 0.95)

# }