conducts statistical tests for each pairwise interaction, indicating whether the effect of the canopy species on recruitment is enhancing (i.e. positive), depressing (i.e. negative), neutral, or whether it could not be tested due to low sample size. IMPORTANT NOTE: Data on recruitment in Open is required for the tests. To assess whether recruitment is affected by a given canopy species compared to the "Open" it is used an exact binomial test or a chi square test (if the number of recruits is large enough so that the expected frequencies are larger than 5). The tests address the null hypothesis that recruitment is as frequent under a given canopy species as it is in open interspaces. Thus, we use these tests as a goodness-of-fit tests. The logic is that, if recruitment were neutral regarding the microhabitat, we would observe that the number of recruits under canopy would be exactly proportional to the relative cover of each microhabitat. If the null hypothesis is rejected, we would conclude that recruitment is affected (enhanced or depressed) by the canopy species compared with the prospects of recruitment when seeds are dispersed away from established plants. When the exact binomial test is applied to canopy-recruit pairs with very low number of recruits, it may be impossible to reject the null hypothesis even if all the recruits occurred in the less likely microhabitat. In such cases, one might conclude that the interaction has a neutral effect when it is actually not possible to reach a conclusion. We classify these cases as "not testable".
Usage
int_significance(int_data, cover_data, int_type = c("rec", "fac", "comp"))Arguments
- int_data
data frame containing interaction data.
- cover_data
data frame with the abundance of each canopy species in each plot.
- int_type
Indicates the type of plant-plant interaction that will be analyzed: general recruitment, recruitment enhancement (i.e. facilitation) or recruitment depression (i.e. competition). Explanation of its options:
rec: All the pairwise interactions observed will be in the output. Focuses on canopy-recruit interactions considering that every recruit growing under the canopy of another plant may occupy that space in the future, thus having a potentially positive effect on the recruit species population. Therefore, even a single observation is considered an interaction. This type of networks considers every species present in the study system, whether as a canopy or as a recruit, as a node in the network. It also includes "Open" as a particular node since some species may recruit away from established plants. Non-detected interactions are also considered since zero frequency can provide evidence of a very negative interaction if the expected frequency under the canopy species is large.
fac: Only those pairwise interactions that resulted in recruitment enhancement will be in the output. Focuses on interactions with a significantly higher recruitment density under canopy than in "Open" (i.e. facilitation). Non-detected interactions are not considered and "Open" is not included as a node, although its relative cover is considered as part of the sampling area.
comp: Only those pairwise interactions that resulted in a recruitment depression will be in the output. Focuses on interactions with a significantly lower recruitment density under canopy than "Open" (i.e. competition). Non-detected interactions are considered (i.e. expanding with 0 all possible interactions in the study system), as the absence of recruitment of a species under a given canopy can reflect a particularly strong depression of recruitment under that canopy species. "Open" is not included as a node, although its relative cover is considered as part of the sampling area.
Value
a data frame with the following information for each recruit-canopy interaction with the following information:
Canopy: Canopy species
Recruit: Recruit species
Fcr: frequency of recruitment, as the number of recruits found under that canopy species.
Ac: Area (in m^2^), (or m when relative cover is measured with transects) occupied by the canopy species in the study site.
Fro: frequency of recruitment in open interspaces.
Ao: Area (in m^2^), (or m when relative cover is measured with transects) occupied by the open interspaces in the study site.
testability: Testability indicates the smallest p-value that a binomial test will estimate based in a given number of recruits. If this p-value is above the reference p-value (typically 0.05), then you have too few cases (number of recruits) to ever reject the null hypothesis, and the interaction is not testable.
Significance: p-value of the chi square or binomial test assessing the null hypothesis that Fcr and Fro are equal to the expected frequencies based on the relative cover of each of the two microhabitats: $Ac/(Ac+Ao)$ and $1-(Ac/(Ac+Ao))$
Test_type: Indicates whether, depending on the sample size, a chi square or binomial test has been conducted.
Effect_int: Indicates, for each interaction, whether the analysis classifies it as "Enhancing", "Depressing", "Neutral" or "Not Testable".
Examples
int_signif_rec <- int_significance(Amoladeras_int, Amoladeras_cover, int_type = "rec")
#> Different tests were used for different canopy-recruit pairs.
#> Check column Test_type
head(int_signif_rec)
#> Canopy Recruit Fcr Ac Fro Ao testability
#> 1 Artemisia_barrelieri Artemisia_barrelieri 3 1 54 6926.992 1.217249e-219
#> 22 Artemisia_barrelieri Artemisia_campestris 0 1 1 6926.992 1.443420e-04
#> 20 Artemisia_barrelieri Asparagus_albus 0 1 7 6926.992 1.305414e-27
#> 16 Artemisia_barrelieri Asparagus_horridus 0 1 18 6926.992 7.397197e-70
#> 12 Artemisia_barrelieri Ballota_hirsuta 0 1 11 6926.992 5.666552e-43
#> 13 Artemisia_barrelieri Helichrysum_stoechas 3 1 796 6926.992 0.000000e+00
#> Significance Test_type Effect_int
#> 1 8.748100e-08 Binomial Enhancing
#> 22 1.000000e+00 Binomial Neutral
#> 20 1.000000e+00 Binomial Neutral
#> 16 1.000000e+00 Binomial Neutral
#> 12 1.000000e+00 Binomial Neutral
#> 13 2.337311e-04 Binomial Enhancing
int_signif_fac <- int_significance(Amoladeras_int, Amoladeras_cover, int_type = "fac")
#> Different tests were used for different canopy-recruit pairs.
#> Check column Test_type
head(int_signif_fac)
#> Recruit Canopy Fcr Ac Fro Ao
#> 1 Artemisia_barrelieri Thymelaea_hirsuta 5 140.930 54 6926.992
#> 2 Artemisia_barrelieri Artemisia_barrelieri 3 1.000 54 6926.992
#> 6 Artemisia_barrelieri Lygeum_spartum 23 643.715 54 6926.992
#> 7 Artemisia_campestris Lygeum_spartum 3 643.715 1 6926.992
#> 8 Artemisia_campestris Thymus_hyemalis 2 522.580 1 6926.992
#> 10 Asparagus_albus Ziziphus_lotus 4 465.195 7 6926.992
#> testability Significance Test_type Effect_int
#> 1 4.819373e-101 6.464030e-03 Binomial Enhancing
#> 2 1.217249e-219 8.748100e-08 Binomial Enhancing
#> 6 3.766203e-83 1.789370e-11 Chi-square Enhancing
#> 7 5.226714e-05 2.302046e-03 Binomial Enhancing
#> 8 3.451949e-04 1.407226e-02 Binomial Enhancing
#> 10 6.130318e-14 3.614203e-03 Binomial Enhancing
int_signif_comp <- int_significance(Amoladeras_int, Amoladeras_cover, int_type = "comp")
#> Different tests were used for different canopy-recruit pairs.
#> Check column Test_type
head(int_signif_comp)
#> Recruit Canopy Fcr Ac Fro Ao
#> 9 Artemisia_barrelieri Ziziphus_lotus 0 465.195 54 6926.992
#> 21 Artemisia_barrelieri Launaea_arborescens 0 473.865 54 6926.992
#> 117 Helichrysum_stoechas Phagnalon_saxatile 0 33.925 796 6926.992
#> 120 Helichrysum_stoechas Lygeum_spartum 4 643.715 796 6926.992
#> 125 Helichrysum_stoechas Maytenus_senegalensis 0 116.450 796 6926.992
#> 127 Helichrysum_stoechas Ononis_natrix 2 244.965 796 6926.992
#> testability Significance Test_type Effect_int
#> 9 1.375793e-65 4.899294e-02 Binomial Depressing
#> 21 3.500431e-65 4.901722e-02 Binomial Depressing
#> 117 0.000000e+00 3.818974e-02 Binomial Depressing
#> 120 0.000000e+00 4.849900e-16 Chi-square Depressing
#> 125 0.000000e+00 2.541056e-04 Chi-square Depressing
#> 127 0.000000e+00 8.545344e-07 Chi-square Depressing