DOI: 10.5061/dryad.4b8gthtn2
title: Leaf area predicts conspecific spatial aggregation of woody species

description: Aim: Addressing how woody plant species are distributed in space can
 reveal inconspicuous drivers that structure plant communities. The spatial
 structure of conspecifics varies not only at local scales across
 co-existing plant species but also at larger biogeographical scales with
 climatic parameters and habitat properties. The possibility that
 biogeographical drivers shape the spatial structure of plants, however,
 has not received sufficient attention. Location: Global synthesis. Time
 period: 1997 - 2022. Major taxa studied: Woody angiosperms and conifers.
 Methods: We carried out a quantitative synthesis to capture the interplay
 between local scale and larger scale drivers. We modelled conspecific
 spatial aggregation as a binary response through logistic models and
 Ripley’s L statistics and the distance at which the point process was
 least random with mixed effects linear models. Our predictors covered a
 range of plant traits, climatic predictors and descriptors of the habitat.
 Results: We hypothesized that plant traits, when summarized by local scale
 predictors, exceed in importance biogeographical drivers in determining
 the spatial structure of conspecifics across woody systems. This was only
 the case in relation to the frequency with which we observe aggregated
 distributions. The probability of observing spatial aggregation and the
 intensity of it was higher for plant species with large leaves but further
 depended on climatic parameters and mycorrhiza. Main Conclusions: Compared
 to climatic variables, plant traits perform poorly in explaining the
 spatial structure of woody plant species, even though leaf area is a
 decisive plant trait that is related to whether we observe homogenous
 spatial aggregation and its intensity. Despite the limited variance
 explained by our models, we found that the spatial structure of woody
 plants is subject to consistent biogeographical constraints and that these
 exceed beyond descriptors of individual species, which we captured here
 through leaf area.
description: On the 8<sup>th</sup> of September 2022 we carried
 out a search in the Web of Science with the search string “(Ripley's
 K function) AND (forest)”. The search yielded 356 hits. We screened those
 356 studies for eligibility, first based on the suitability of their
 article titles and second based on their abstracts (Figure S1). The 240
 eligible studies were subsequently screened manually upon reading the
 entire article based on the following inclusion criteria:
 (1)           The study reported on univariate Ripley's K or
 L statistics or else it was possible to extract those from figures or
 maps. (2)           The study had been carried out in a
 woody ecosystem or a rangeland. (3)           The
 univariate Ripley’s K statistics described the distribution of individuals
 from a single plant species. (4)           The authors
 named the plant species for which the univariate Ripley's K
 statistics had been described. (5)           The
 landscape (for example a logging area) did not induce conspicuous point
 processes that could not be corrected within the analysis.
 We manually processed the remaining 240 studies through reading
 the main text which reduced the final number of eligible studies to 69. A
 list of those data sources can be found in Appendix Three. From those
 studies we extracted the following moderators and we fitted them as
 predictors in subsequent models: <em>Mean annual
 temperature</em>: continuous variable. When unreported, we extracted
 the variable based on coordinates from WorldClim (Fick & Hijmans,
 2017). <em>Total annual precipitation</em>:
 continuous variable. When unreported, we extracted the variable based on
 coordinates from WorldClim (Fick & Hijmans, 2017).
 <em>Latitude of the study location</em>: continuous
 variable. When unreported, we extracted the information based on the
 closest location reported. <em>Longitude of the
 study location</em>: continuous variable. When unreported, we
 extracted the information based on the closest location
 reported. <em>Site area</em>: continuous
 variable. We extracted the site area from the studies and converted it
 into a unified unit, square meter. <em>Tree
 species</em>: categorical variable.
 <em>Plant traits</em>: we collected data on 7 traits:
 leaf area (i.e. the size of the leaves), seed mass, wood density, leaf
 mass per area, tree height, plant species biomass and stem specific
 density. We first gathered data on tree height, seed mass and leaf area
 from the subset of common species in TRY (Díaz et al., 2022). We
 subsequently searched for seed mass data the SID database (Royal Botanic
 Gardens Kew, 2023) and the ICRAF database for wood density data
 (Ketterings et al., 2001). In the cases we observed no records in those
 databases we checked the EOL database (http://eol.org.). For leaf
 area, leaf mass per area, tree height, plant species biomass and stem
 specific density, we extracted them from the EOL database (http://eol.org.). We opted
 with these traits to cover as many trait syndromes as possible but the
 main criterion which we used to decide on the traits was the feasibility
 of acquiring them for the plant species in our database.
 <em>Woody system age</em>: categorical variable. We
 classified non woody habitats, plantations and systems that had recently
 experienced serious disturbances as “young” whereas natural forests or
 woody stands that had reached maturity as “old”.
 <em>Mycorrhiza type</em>: categorical variable. We
 extracted mycorrhizal types for each species from Wang and Qiu (2006). In
 the cases that we could find no mycorrhizal classification information in
 the database at a species level we searched instead the database compiled
 by Delavaux et al. (2021) containing information at a genus level. We only
 extracted mycorrhizal classifications if these supported a single
 mycorrhizal type at a minimum probability of 85%. Otherwise, we left the
 plant species unclassified in relation to mycorrhiza.
 <em>Ripley's L effect size: </em>continuous
 variable. We first calculated for all distances the ratio between the (1)
 difference between the Ripley's L statistic and the width of the 95%
 CI envelope divided by two and (2) the difference between the upper and
 lower points of the envelope divided by two. A large absolute value
 suggests a strong deviation from randomness whereas any value below 1
 suggest a random process. We identified the location where the absolute
 value of this ratio was maximum.
 <em>Ripley's L statistic</em>:<em>
 </em>continuous variable. We transformed Ripley´s K statistics (when
 they had not been transformed) into Ripley´s L statistics. We only used
 the value at the location where we observed the maximum in absolute value
 <em>Ripley's K</em> effect size.
 <em>Distance when Ripley's L
 peaked</em>:<em> </em>continuous variable describing the
 distance at which we observed the maximum in absolute value Ripley´s L
 effect size. <em>Köppen climate zone</em>:
 a categorical variable with 4 levels describing the main climatic zones
 based on the Köppen<em> </em>classification:
 <em>A</em> (tropical climates); <em>B</em> (arid
 climates); <em>C</em> (temperate climates);
 <em>D</em> (continental climates). We extracted those from the
 raster files published by Beck et al. (2018). In the
 cases that we observed multiple values in databases (referring here mainly
 to plant trait values) per species, we used the median value. In the cases
 when we had to digitize plots to extract data, we did so with Plot
 Digitizer v2.6.8.
description: # Leaf area predicts conspecific spatial aggregation of woody species
 [https://doi.org/10.5061/dryad.4b8gthtn2](https://doi.org/10.5061/dryad.4b8gthtn2) We present the dataset on which we base our meta-analysis and the code that we use to arrive to the results in the article. ## Description of the data and file structure | ID | Study ID | | :-------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------- | | Authors | The authors of the study | | Article.Title | The article title | | Abstract | The Abstract of the article | | Source.Title | Journal name where the article got published | | Publication.Year | Year of publication | | alpha.diversity.of.the.site | alpha diversity of the site | | tree.species | species for which the statistics got calculated | | investigation.area..square.meters. | plot size | | relative.abundance.of.the.tree | relative abuyndance of the species studied | | relative.abundance.of.the.tree....individuals. | number of individuals of the tree | | latitude | latitude of the site | | longitude | longitude of the site | | habitat.type | type of forest | | investigation.area..square.meters..1 | plot size converted into square meters | | year | year when the observation was carried out | | main.statistic | Ripley's L statistic - max value observed | | ES | respective effect size when corrected against the expectations of a random distribution | | distance | distance at which Ripley statistics peaked | | area | this is again the plot size | | management.status..e.g..natural.forest..managed.forest..grazed.meadow\.etc. | this is again the type of forest | | sera | forest age: two levels old and new | | exclude | if this observation should get excluded in the analysis | | Notes | any notes | | X | ignore this column | | X.1 | ignore this column | | temperature | mean annual temperature | | precipitation | total annual precipitation | | climate | Köppen climate zone | | mycorrhiza | mycorrhizal association type of the species | | wood.density | wood density of the species | | seed.mass | seed mass of the species | | height | maximum plant height of the tree species | | ssd | stem specific density for the species | | lma | leaf mass per area for the species | | biomass | maximum tree biomass | | cN | nitrogen content of species biomass | | leafarea | average leaf area | | conifer | is the tree a conifer | | location | a unique grouping ID of different locations | | area2 | log transformation of site area | | main.statistic2 | log transformation of the Ripley's L value | | lma2 | log transformation of the LMA value | | leafarea2 | log transformation of the leaf area value | | biomass2 | log transformation of the biomass value | | seed.mass2 | log transformation of the seed mass value | | climate2 | only the first later from the Köppen climate zone | | precipitation2 | transformation of the precipitation data | | genus | the genus of the species | | conifer2 | classification of species into conifers vs evergreen vs decidious | #### Code/Software Data-analysis was carried out on R. We attach the code. Data are presented in a form optimised for use with R and not for reading.