Field |
Response |
Study title |
Swash level selects functionally specialized assemblages
of beach interstitial meiofauna (Platyhelminthes,
Proseriata) |
Authors |
Alejandro MartÃnez, Diego Fontaneto, Marco
Curini-Galletti |
Hypothesis |
Species responses to beach hydrodynamics depend on the
presence of certain traits, with the main rationale included in the two
opposite scenarios of environmental filtering or functional trait
selection. (1) We show that species richness does not differ across
beach levels but depends on the characteristics of each beach, using
spatially explicit models. (2) We demonstrate that species composition
across beach levels depends on the species traits, in addition to
geographical and abiotic factors by fitting multivariate generalized
linear models. (3) We apply null modelling to highlight that the overall
species functional space is smaller than the expected one in the swash
level but not in the shoaling and subtidal levels, suggesting an effect
of trait-based ecological filtering in the swash level. Species
functional contribution to the functional space is also higher in the
swash level, reflecting a lower degree of functional redundancy. (4) We
show that the observed differences in functional spaces depend on the
higher frequency of hydrodynamics-related traits in the species of the
swash level, using binomial generalized linear models. |
Ecological unit |
Reflective sandy beaches |
Power analysis |
No |
Focal taxa |
Proseriata (Platyhelminthes) |
Resolution |
Species level |
Number of taxa |
152 |
Sampling unit |
Three beach levels (swash, shoaling and subtidal) within
each beach |
Number of sampling units |
348 sites in 116 beaches |
Sampling effort |
1130 unique occurrences |
Occurrence data type |
Presence-only |
Number of traits |
16 |
Continuous traits used |
2 |
Discrete traits used |
10 |
Binary traits used |
4 |
Fuzzy-coded traits used |
0 |
Trait resolution |
Coarse |
Sample size per species and trait |
Species level ; Mean value at the
species level |
Hypothesized function of each trait |
Morphological traits related to
interstitial adaptation, ecology, trophic niche, and
reproduction |
Intraspecific variation accounted for? |
No |
Data source |
Original measurements |
Data exploration |
Data visualization, Collinearity assessment, Missing
data assessment, Species sampling coverage |
Collinearity assessed? |
We used Gower distance and principal coordinate
analysis to extract three trait axes for analyses. |
Transformations done? |
Scaled and centred continuous
traits |
Missing data accounted for? |
Yes |
Imperfect detection control |
No |
Functional space method |
Probabilistic hypervolume |
Dissimilarity metric used for functional space |
Gower |
Level of analysis |
Alpha diversity (within group), Beta diversity
(between group) |
FD method |
Richness, Species contribution to richness. |
Model |
Spatially explicit generalized linear models, multivariate
generalized linear models, binomial generalized linear models, Mantel
test; Null-modelling |
Effect sizes |
Standard Effect Size (SES). |
Model support |
SES |
Model uncertainty |
Non applicable |
Validation method |
Non applicable |
Preregistration |
No |
Code link |
Upon acceptance |
Community data link |
Upon acceptance |
Trait data link |
Upon acceptance |
Environmental data link
|
WorldClim 2 database:
http://www.worldclim.com/version2
Copernicus database:
https://www.copernicus.eu/en/access-data
|
Data and Code description |
Software and package version numbers;
commented code. The code we provided can reproduce all results, figures
and tables. |
Date |
19 March 2023 |