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Fig/fig wasp, yucca/yucca moth, orchid/orchid bee: co-evolutionary relationship, Reciprocal mutualism (Eltz et al. 2011)

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Indirect facilitative pathway b/w plants and pollinators = neighbour-mediated pollinator facil.(magnet hypothesis) (Laverty 1992). Competition b/w plants for resources incl. pollinators. Diverse floral displays = net increase in pollinator frequency/diversity (Wirth et al. 2011)

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Data includes observational counts/abundance data, est. of pollen lim., seed/fruit set, visitation rates & diversity measures. Would benefit highly from synthesis to determine most effective methodologies for dealing with this data

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Way of understanding the level of complexity of stats approaches used in pollinator facil. lit. and allows for ID of stats methods that have potential for increased citations

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Thomson Reuters web of science used to conduct search using the term “plant AND pollinat* AND facilitate*”. Refined by research areas: environmental sciences, ecology, plant sciences.

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Studies without primary data (notes, reviews, meta-analyses) and studies on non-pollinating insects were excluded

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Fig1. Multiple polynomial regressions of the change in the use of tests over time. (n=245, ANOVA: R2=0.46308, mean (SD/SE): R2=0.32680, regression/correlation: R2=0.50553, t-test: R2=0.16791, mixed effects model: R2=0.41467, chi-square R2=0.34096, re-sampling: R2=0.22495, ordination: R2=0.38394, none: R2=0.15419, other: R2=0.16871).

P facilitation primarily used only mean/SE/SD. ANOVAs used most frequently from 2000-14. Regression= steepest increase in use and along w/ means (SD/SE) were second most highly used test after ANOVAs. Mixed effects models, ordinations &chi-square tests also highly cited. Studies that used no/other statistics were low frequency.

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Fig2. Breakdown of the tests used in sub-disciplines of pollinator facilitation: neighbour-effect, pollination biology, invasion biology, agriculture and other (herbivory, behaviour, trophic cascades and evolution). A) Total frequency of studies that utilized these statistical test groups in each sub-discipline (N=245). B) Relative proportion of studies that utilized these statistical test groups in each sub-discipline. The number of studies in each category (n) is shown (N=245).

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Fig3. Frequency and average number of total citations for each group of statistical tests used in pollinator facilitation studies (n=95). Tests are grouped according to having A) relatively high citations compared to publication frequency, B) relatively equal citations compared to publication frequency, C) Relatively low citations cf. to publication frequency.

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Fig4. A linear regression for the relationship between the diversity of statistical tests used (the number of unique statistical tests used per study) and the average citations per year in the discipline of pollinator facilitation (n=95, R= 0.11517)

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ANOVAs, regressions/correlations and ordinations increasing quickly. St

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Early on, studies on pollinator facilitation primarily utilized measurements of only mean and SE/SD. \ANOVAs used most frequently from 2000-2014. Regressions had steepest increase in use and along with means (SD/SE) represent the second most highly used tests after ANOVAs. Mixed effects models, ordinations and chi-square tests also increased in use over the last 35 years. Studies that used no statistics and ‘other’ statistics were low in frequency.

- 1. STATISTICAL TESTS USED IN PLANT-POLLINATOR FACILITATION STUDIES Ally Ruttan
- 2. Introduction Many plants rely on insects for pollination and co-evolution is common Facilitation between plants and pollinators = shared access to resources
- 3. Introduction – obligate pollinator mutualisms Rely exclusively on each other for pollination/reproduction E.g. fig & fig wasp, yucca & yucca moth, orchids & orchid bees
- 4. Introduction – pollinator facilitation Target Co-flowering species + = Pollinators + =
- 5. Purpose Data in this field takes on many forms… This review examines the statistical scope of the literature to date on the study of pollinator facilitation.
- 6. Objectives 1) Determine the change in use of statistical tests over time 2) Compare use of statistical tests for various sub-disciplines of pollinator facilitation 3) Compare publication frequency and average citations per publication for different test groups 4) Contrast the relationship between diversity of statistical tests and citation rate
- 7. Search technique plant AND pollinat* AND facilitat* Refined by: Environmental science, ecology, plant science, English-only 496
- 8. Search technique plant AND pollinat* AND facilitat* Refined by: Environmental science, ecology, plant science, English-only 95
- 9. How often do we use stats? Yes 97% No 3%
- 10. Test usage over time… 12 10 8 6 4 2 0 1979 1984 1989 1994 1999 2004 2009 2014 Frequency Year ANOVA mean (SD/SE) regression/Correlation t-test mixed effects model chi-square re-sampling ordination none other
- 11. Test usage by topic/discipline 70 60 50 40 30 20 10 0 Frequency 5 11 4 2 1 8 18 5 2 2 1 2 36 16 25 10 11 4 2 2 3 1 17 14 10 14 12 3 4 2 1 2 3 1 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Percentage neighbour-effect pollination biology invasion biology agriculture other A. B.
- 12. Publications vs. citations by test 60 50 40 30 20 10 0 Publications Average citations Number of studies 60 50 40 30 20 10 0 Publications Average citations Number of studies 60 50 40 30 20 10 0 Publications Average citations Number of Studies
- 13. Test diversity vs. citation rate R² = 0.1152 18 16 14 12 10 8 6 4 2 0 0 1 2 3 4 5 6 Average citations/year Diversity of tests
- 14. Key Results ANOVAs, means, and regressions/correlations used most frequently Neighbour-effect and invasion biology studies used more extensive tests compared to pollination biology Ordinations, re-sampling techniques and t-tests received the most citations
- 15. Novel pathways… Rarefaction curves/ordinations should be implemented Effect size estimates rarely used; useful for teasing apart facilitative vs. competitive effects Network analyses to highlight important pathways and unanticipated interactions
- 16. But… Must still take experimental design into account Not all tests can be applied in every scenario Test assumptions must be met

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