Fitness differences override variation-dependent coexistence mechanisms in California grasslands
Data files
Nov 13, 2024 version files 450.10 KB
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Competition_allometric_clean.csv
504 B
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Competition_combined_clean.csv
437.15 KB
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README.md
12.45 KB
Abstract
While most studies of species coexistence focus on the mechanisms that maintain coexistence, it is equally important to understand the mechanisms that structure failed coexistence. For example, California annual grasslands are heavily invaded ecosystems, where non-native species have largely dominated and replaced native communities. These systems are also highly variable, with a high degree of rainfall seasonality and interannual rainfall variability – a quality implicated in the coexistence of functionally distinct species. Yet, despite the apparent strength of this variation, coexistence between native and non-native annuals in this system has faltered. It is therefore uncertain to what degree rainfall variation can offset average fitness differences between native and non-native annual plants in California grasslands to promote coexistence, nor what coexistence mechanisms are most relevant. To test these dynamics, we implemented a competition experiment between five annual species of native forbs and non-native grasses. We grew individuals from each species under varying densities of all other species as competitors, under either wetter or drier early-season rainfall treatments. Using subsequent seed production, we parameterized competition models, assessed the potential for coexistence among species pairs, and quantified the relative influence of variation-dependent coexistence mechanisms. Overall, we found little potential for coexistence. Competition was dominated by the non-native grass Avena fatua, while native forbs were unable to invade non-native grasses. Mutual competitive exclusion was common across almost all species and often contingent on rainfall, suggesting rainfall-mediated priority effects. Among variation-dependent mechanisms, the temporal storage effect had a moderate stabilizing effect for most species, while relative nonlinearity in competition was largely destabilizing, except for the most conservative non-native grass, which benefited from a competitive release under dry conditions. Our findings suggest that rainfall variability does little to mitigate the fitness differences that underlie widespread annual grass invasion in California, but that it influences coexistence dynamics amongst the now-dominant non-native grasses.
https://doi.org/10.5061/dryad.j3tx95xqk
Description of the data and file structure
This data was collected in a field experiment implemented 2016-2017 (Brown’s Valley California), in which phytometers of each of six species were grown in a pairwise competitive background of one of the other competitors. They were grown under two treatments: rainfall exclusion and no rainfall exclusion over the growing season (October to April) and then harvested to measure growth and seed production.
Files and variables
DATA-SPECIFIC INFORMATION FOR: Competition_allometric_clean.csv
Description: OLS linear regression intercept and beta estimate (slope) with associated statistics for each of 6 species allometric regressions. One regression run per species using form ‘lm(seeds ~ 0 + biomass)’. Because we did not see strong significant differences in allometry based on fall drought condition (fall dry vs. control) for any species, we pooled specimen data collected in each condition for each species regression. We fixed the intercept at 0 for each model (0 seeds when biomass is 0) so no statistics are present for the intercept (NA values present in intercept statistics columns). The intercept value column is retained in the dataset to document 0 for all models.
Number of variables: 7
Number of cases/rows: 6
Variable List: <variable name: type, description, unit, and value labels as appropriate>
1. species: nominal, 4-letter code of species, unitless, values possible: “AVFA”, “BRHO”, “ESCA”, “LACA”, “TRHI”, “VUMY”
2. intercept: integer, zero-intercept of allometric regression model, seeds/gram, values possible: 0
3. intercept_pval: real number, p-value of allometric linear regression intercept, unitless, values possible: NA when intercept fixed at 0
4. intercept_se: real number, standard error of allometric linear regression intercept, seeds/gram, values possible: NA when intercept fixed at 0
5. slope: real number, beta estimate of allometric linear regression model, seeds/gram, values possible: unbounded continuous real number
6. slope_pval: real number, p.value of allometric linear regression beta estimate, unitless, values possible: continuous probability value, range 0-1
7. slope_se: real number, standard error of allometric linear regression beta estimate, seeds/gram, values possible: unbounded continuous real number
Missing data codes: <code/symbol, definition>
NA, not applicable
Specialized formats or other abbreviations used:
Species codes: AVFA = Avena fatua, BRHO = Bromus hordeaceus, ESCA = Eschscholzia californica, LACA = Lasthenia californica, TRHI = Trifolium hirtum, VUMY = Vulpia myuros
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DATA-SPECIFIC INFORMATION FOR: Competition_combined_clean.csv
Dataset description: Phytometer and background competitor stem counts, biomass, flower counts for select forbs, and disturbance notes, with with OLS allometric regression intercept, beta estimate, estimated phytometer fecundity, and associated statistics for fecundity estimates. Intercept and beta estimate for each species come from the dataset ‘Competition_allometric_cleaned.csv’.
The data are in wide format and redundancies are important to recognize prior to analyzing the data. Rows in the dataset represent background competitor-phytometer pairings at the sub-subplot level. Background competitor data are repeated across all phytometers in a sub-subplot. In this dataset, allometric fecundity was estimated for phytometers in two ways: (1) using mean individual biomass of phytometers harvested for ANPP, and (2) using the projected total biomass of all phytometers for a species present in the sub-subplot. Which model was used is indicated in the column ‘pfit_source’. Therefore, phytometer data are partially repeated twice: there are 2112 rows in the dataset, but only 1056 unique phytometer observations from field data collection. Phytometer stems counts, biomass, and disturbance notes are repeated twice per phytometer observation, but estimated fecundity for the phytometer is unique across rows.
Data quality comments: Some counts of phytometers present in the sub-subplot were greater than the expected number of seeds sown (e.g., variable ‘insitu_pstems’ > 15), in particular for B. hordeaceus and L. californica. Phytometer seeds were not counted prior to seeding in Oct 2016, rather seeds were added by visual estimation. It is possible for 7 field observations in the dataset with insitu_pstems > 15 (14 total rows due to data redundancy), more seeds than intended were sown and/or stems from the same individual were counted in the field as multiple individuals. In a few cases for B. hordeaceus, after harvesting we recognized an individual clipped was actually multiple individuals but it was not possible to separate them without roots attached and so we updated the stem count.
7 phytometer biomass collections were erroneously skipped in the field. These phytometers were either not counted and not harvested, or counted but not harvested. Their corresponding records in the datasets (14 rows total from data redundancy) have NA values in columns ‘pdry_wgt_g’, ‘p.ind.wgt.g’, ‘p_totwgt’, and in some fecundity estimate regression columns. Phytometers that were counted but not harvested have data present in phytometer stem count columns even though biomass data are missing.
Number of variables: 34
Number of cases/rows: 2112
Variable List: <variable name: type, description, unit, and value bounds as appropriate>
1. plot: ordinal, numbered experimental plots 1-16 west to east, unit NA, values possible: 1-16
2. falltreatment: nominal, dry (sheltered from rainfall) or wet (ambient) precipitation treatment in fall germination window (Sep - Jan), unit NA, values possible: “dry”, “wet”
3. treatment: nominal, seasonal precipitation treatment (consistent = sheltered from rainfall all year, fall = sheltered fall only, spring = sheltered spring only, control = never sheltered), unit NA, values possible: “consistentDry”, “fallDry”, “springDry”, “controlRain”
4. shelterBlock: nominal, alphabetic experimental block A-D west to east, unit NA, values possible: “A”, “B”, “C”, “D”
5. shelter: boolean, whether plot had rainout shelter (1) or no shelter (0), unit NA, values possible: 0, 1
6. background: nominal, scientific name (genus, species epithet) of background competitor seeded, unit NA, values possible: “Avena fatua”, “Bromus hordeaceus”, “Lasthenia californica”, “Eschscholzia californica”, “Trifolium hirtum”, “Vulpia myuros”, NA when no background competitor seeded
7. bcode4: nominal, 4-letter code of background competitor species or ‘Control’ to indicate no background competitor seeded, unit NA, values possible: “AVFA”, “BRHO”, “ESCA”, “LACA”, “TRHI”, “VUMY”, “Control”
8. bdensity: nominal, categorical seeding density (low, high) of background competitor, unit NA, values possible: “low”, “high”, NA when no background competitor seeded
9. b.ind.wgt.g: real number, average aboveground biomass of individual background competitor (average of competitor replicates), grams, values possible: 0 or positive real number, NA when no background competitor seeded
10. plot_banpp: real number, aboveground biomass of background competitor at one meter^2 scale (calculated from: b.ind.wgt.g * insitu_plot_bdensity), grams, values possible: 0 or positive real number, NA when no background competitor seeded
11. disturbed_banpp: nominal, background competitor biomass disturbed (e.g., browsing by deer, gopher disturbance) – only disturbance (1) is noted all else is NA, unit NA, values possible: NA (not disturbed), 1 (disturbed)
12. insitu_plot_bdensity: whole number, background competitor density per meter^2, individuals per 1 meter^2, values possible: 0 or positive integer, NA when no background competitor seeded
13. insitu_plot_bflowers: whole number, background competitor flowers per meter^2 (only available for forb species), flowers per 1 meter^2, values possible: 0 or positive integer, NA when no background competitor seeded
14. insitu_bdisturbed: nominal, background competitor density disturbed (e.g., from rainfall, gopher, browsing) – only disturbance (1) is noted all else NA, unit NA, values possible: NA (not disturbed), 1 (disturbed)
15. seedsAdded: whole number, estimated background competitor seeds sown (calculated as: grams seeds sown divided by mean grams individual seed, where mean individual seed weight calculated from 3 reps of 30 seeds), values possible: positive integer, NA when no background competitor seeded
16. perPersist: real number, percent persistence of seeds that reached mature plant stage (calculated as: (insitu_plot_density/seedsAdded)*100), percent, values possible: real number in range 0-100, NA when no background competitor seeded
17. bdensity_flag: nominal, flag percent persistence value as questionable, unit NA, values possible: 1 (flag) or NA (no flag)
18. phytometer: nominal, scientific name (genus, species epithet) of phytometer, unit NA, values possible: “Avena fatua”, “Bromus hordeaceus”, “Lasthenia californica”, “Eschscholzia californica”, “Trifolium hirtum”, “Vulpia myuros”
19. pcode4: nominal, 4-letter code of phytometer species, unit NA, values possible: “AVFA”, “BRHO”, “ESCA”, “LACA”, “TRHI”, “VUMY”
20. pdry_wgt_g: whole number, total aboveground biomass of phytometer specimens collected, grams, values possible: 0 or positive real number
21. pANPP_stems: whole number, count of phytometer specimens collected for biomass sample, individuals, values possible: 0 or positive integer
22. p.ind.wgt.g: real number, mean aboveground biomass of individual phytometer (calculated as: pdry_wgt_g/pANPP_stems), grams, values possible: 0 or positive real number
23. pANPP_disturbed: boolean, whether phytometer biomass disturbed (e.g., by runoff, gopher) (1) or not (0), unit NA, values possible: 0, 1
24. insitu_pstems: whole number, count of phytometers present in field (may exceed count of individuals harvested for phytometer biomass), individuals, values possible: 0 or positive integer
25. insitu_pdisturbed: boolean, whether phytometer estimated stem counts disturbed (e.g., by runoff, gopher) (1) or not (0), unit NA, values possible: 0, 1
26. p_totwgt: real number, projected total biomass of phytometers in field (calculated as: p.ind.wgt.g * insitu_pstems), grams, values possible: 0 or positive real number
27. intercept: integer, zero-intercept of allometric regression model, seeds/gram, values possible: 0
28. slope: real number, beta estimate of allometric linear regression model, seeds/gram, values possible: unbounded continuous real number
29. p_seedfit: real number, estimated number of phytometer seeds from allometric regression (predicted values from regression), seeds, values possible: 0 or positive real number, or NA for 14 rows with missing phytometer biomass
30. lwrCI.95: real number, lower boundary of 95% confidence interval for phytometer fecundity estimate from allometric regression, seeds/gram, values possible: unbounded real number, or NA for 14 rows with missing phytometer biomass
31. uprCI.95: real number, upper boundary of 95% prediction interval for phytometer fecundity estimate from allometric regression, seeds/gram, values possible: unbounded real number, or NA for 14 rows with missing phytometer biomass
32. lwrPI.95: real number, lower boundary of 95% prediction interval for phytometer fecundity estimate from allometric regression, seeds/gram, values possible: unbounded real number, or NA for 14 rows with missing phytometer biomass
33. uprPI.95: real number, upper boundary of 95% prediction interval for phytometer fecundity estimate from allometric regression, seeds/gram, values possible: unbounded real number, or NA for 14 rows with missing phytometer biomass
34. pfit_source: nominal, biomass data source for phytometer fecundity estimates from allometric regression: mean phytometer biomass (p.ind.wgt.g) or projected total biomass of phytometers in field (p_totwgt), unit NA, values possible: “p.ind.wgt.g”, “p_totwgt”
Missing data codes: <code/symbol, definition>
NA, not applicable or missing
Specialized formats or other abbreviations used:
none