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Supplementary Material
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This material is available as part of the online article from:
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Ozinga et al. Dispersal failure contributes to plant losses in NW Europe
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http://www.blackwell-synergy.com/doi/full/10.1111/j.1461-0248.XXXX.XXXXX.x
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Appendix S1 Check for possible confounding effects
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Appendix S2 Historical overview of changes in dispersal infrastructure in the landscape
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of Northwest Europe.
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Please note: Blackwell Publishing is not responsible for the content or functionality of
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any supplementary materials supplied by the authors. Any queries (other than missing
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material) should be directed to the corresponding author for the article.
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Appendix S1: Check for possible confounding effects
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The robustness of the results was tested for the Dutch dataset, since this dataset
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has been recorded with the highest resolution and has the smallest proportion of missing
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values (see Table S1) and changes in frequency of occurrence are more marked at more
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detailed spatial scales (Thomas & Abery 1995, Kunin 1998, Witte & Torfs 2003, Tamis
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2005).
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Correlations between variables
A potential problem in evaluating the importance of individual variables is that
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they might be interrelated (multicollinearity). We checked for this potentially
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confounding effect in two ways.
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Firstly, we calculated Pearson correlations between the explanatory variables.
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Plant characteristics were only weakly correlated between species (r < 0.25 for all
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combinations, with the highest correlations between ‘Dispersal potential – dung’ * ‘Seed
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longevity’: r = 0.23 and ‘Nitrogen requirements’ * ‘Seed longevity’: r = 0.22).
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Secondly, we used a combination of conditional and marginal tests (e.g.
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McCullagh & Nelder 1989). In marginal testing, the variable is added to the simplest
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regression model (only including the constant) whereas in conditional testing, the
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variable is entered in the full model (the constant and all other significant variables
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except the variable of interest). If the contributions of the variables of interest are similar
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in both tests, this implies a reliable estimate of the relative importance of the given
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variable. Table S1 indicates that multicollinearity was not a problem. Interaction effects
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were insignificant and did not change the effect of the dispersal vectors on the risk of
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species decline.
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The difference in response between species with a high capacity for dispersal by
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the dung of large mammals and those dispersed by their fur seems surprising at first
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glance. This finding can be understood from the fact that species dispersed in dung are
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generally less specialized in terms of dispersal attributes than those with specialised
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attributes for dispersal in fur (Janzen 1984; Pakeman et al. 2002; Couvreur et al. 2005),
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and are thus less dependent on the availability of large herbivores than species with fur-
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assisted seed dispersal. In addition the dung of cattle kept in stables is widely distributed
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across agricultural lands, including the seeds inside. The cattle themselves, however often
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do not leave the stables any more, and neither do the seeds attached to their fur.
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Table S1 Results of marginal and conditional testing of individual variables and
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performance of variables in the ‘environmental model’ and the ‘dispersal model’.
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Plant characteristic
Frequency in historical species pool
Conditional testing
Environmental model
Wald χ2
Marginal testing
Sign.
R2
Wald χ2
Sign.
Wald χ2
Sign.
67.4
<0.001
0.095
56.2
<0.001
60.6
<0.001
100.7
<0.001
0.150
68.6
<0.001
95.1
<0.001
6.4
0.011
16.1
<0.001
Light requirements
12.4
<0.001
0.018
Dispersal potential – water
39.5
<0.001
0.053
30.1
Dispersal potential – wind
11.1
0.001
0.018
7.5
Dispersal potential – fur
67.0
<0.001
0.090
47.8
Dispersal potential – dung
21.8
0.006
0.031
Dispersal potential – birds
7.6
<0.001
0.013
Nitrogen requirement
Moisture
n.s.
No LDD
64.1
<0.001
<0.001
<0.001
42.1
<0.001
0.006
6.5
0.011
<0.001
40.7
<0.001
n.s.
n.s.
6.9
0.009
n.s.
9.6
0.002
55.1
<0.001
n.s.
0.089
27.7
Sign.
70.870
n.s.
n.s.
Seed longevity
Dispersal model
Wald χ2
<0.001
n.s.
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Correlation with other environmental factors
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Dispersal services for propagules may be correlated with living conditions for
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established plants in their environment. In particular, dispersal services by water may
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correlate with the moisture environment of the established plants, while dispersal services
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by large mammals may correlate with the light conditions in open, grazer-dominated
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vegetation (Ozinga et al. 2004). To evaluate the role of the environment of the
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established plants (in terms of moisture, light and nitrogen), as compared to that of the
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dispersal services, we calculated two models: an ‘environmental model’ (excluding
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dispersal characteristics), and a ‘dispersal model’ (excluding the environmental variables;
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see Table S1). Moisture and light requirements of plant species were obtained from the
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corresponding Ellenberg indicator values. These indicator values are species-specific
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scores, ranging from 1-9, for the optimal occurrence of species along environmental
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gradients, as explained in the main document for nitrogen (Ellenberg et al. 2001). We
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acknowledge that these indication values only represent a proxy of complex habitat
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requirements of species. Evidence for the accuracy of Ellenberg indicator values,
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however, has been provided by several studies reporting a close correlation between
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average indicator values and corresponding measurements of environmental variables
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(see Diekmann 2003 for a review).
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Table S1 indicates that light requirements was only significant in the marginal
testing and not significant in conditional testing and in the environmental model (due to a
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correlation with nitrogen requirements). Moisture requirements was significant in the
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conditional model and the environmental model but it had a limited explanatory power as
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compared to dispersal potential by water (Wald χ2 6.4 respectively 30.1; see Table S1).
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Moreover we found that the dispersal model performed better than the environmental
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model (Nagelkerke’s R2 = 0.32 and 0.23, respectively; Table S1). This means that
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relationships between species trends and dispersal services cannot be explained as
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correlations driven by an underlying correlation between trends and the environment of
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the established plants.
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Phylogenetic non-independence
The observed patterns might be partly phylogenetically induced if related species
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have similar characteristics and extinction risks due to their common ancestry
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(phylogenetic conservatism, e.g. Harvey & Pagel 1991). In order to check for possible
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confounding effects of such phylogenetic non-independence, we performed a post-hoc
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test of bivariate relationships between each of the independent variables and the species
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trend, using phylogenetically independent contrasts (Harvey & Pagel 1991).
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Phylogenetically independent contrasts are comparisons between sister taxa, each
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comparison describing the outcome of a separate, i.e. independent, evolutionary
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divergence of lineages. The contrasts were calculated exclusively between extant species,
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using the ‘Brunch’ routine of the CAIC computer program (Purvis & Rambaut 1995;
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following Burt 1989). This method does not make any assumptions about the mode of
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trait evolution and does not try to reconstruct ancestral states, making it suitable for
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dichotomous variables and permitting analysis by sign tests (Purvis & Rambaut 1995;
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Prinzing et al. 2002). The results (Table S2) largely confirmed our above analysis across
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species as independent data points. For all variables except dispersal capacity by birds,
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the relationship with the risk of decline was significant and in the same direction as in the
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across-species analysis.
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Table S2 Test results for bivariate comparisons across phylogenetically independent
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contrasts. The numbers of contrasts with non-zero differences between the species were
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compared. The table shows the percentage of cases in which these differences were
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positive (i.e. where the declining species had a higher value than the non-declining
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species), and the corresponding Z and P values (two-tailed). Note that only dispersal
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potential for fur and dispersal potential for water were overrepresented among declining
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species.
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N contrasts
Percentage
Z-value
Sign.
Frequency in historical species pool
200
27.5
6.29
<0.0001
Nitrogen requirements
195
28.7
5.87
<0.0001
Dispersal potential – fur
65
78.5
4.47
<0.0001
Dispersal potential – water
89
73.0
4.24
<0.0001
121
24.8
5.50
<0.0001
Dispersal potential – birds
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36.4
0.60
0.5565
Dispersal potential – wind
33
9.1
4.53
<0.0001
Dispersal potential – dung
65
32.3
2.73
0.0064
No LDD
68
32.4
2.79
0.0053
Seed longevity
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1
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