DOI: 10.5061/dryad.1ns1rn90n
title: Functional biogeography of coastal marine invertebrates along the south-eastern Pacific coast

description: Characterizing the spatial structure of taxonomic and functional diversity
 (FD) of marine organisms across regional and latitudinal scales is
 essential for improving our understanding of the processes driving species
 richness and those that may constrain or enhance the set of species traits
 that define the functional structure of communities. Here, we present the
 functional diversity of coastal invertebrate macrofaunal species along the
 south-eastern Pacific, from 7°N to 56°S, we describe spatial
 variation of species traits, and examine the relationship with
 environmental variables. We define the functional traits and the
 distribution range of 2350 marine macroinvertebrates to calculate eight
 metrics of FD. Random forest regression was applied to identify
 significant relationships between FD and six environmental variables.
 Finally, functional ß-turnover was estimated to detect alongshore shifts
 in functional structure and their coincidence with biogeographical
 domains. In contrast with taxonomic richness, measures of trait
 differences, functional space and functional specialisation increase with
 latitude, while functional evenness exhibits a humpback shape, peaking at
 mid-latitudes. Functional redundancy decreases significantly poleward,
 while indications of vulnerability increase. In contrast to taxonomic
 richness, FD was tightly connected to variables indicative of stress and
 productivity, such as dissolved oxygen and nutrients. Sea surface
 temperature and coastal area best explained the increased FD redundancy
 towards the tropics. The high spatial correlation between taxonomic and
 functional ß-turnover suggests environmental filters play an important
 role in the functional structure of the seascape. Our findings
 suggest that processes favouring taxonomic richness are latitudinally
 divergent from those favouring functional diversity. Correlations with
 environmental variables suggest that increased sea surface temperature and
 measures of stability increase redundancy, while variation in dissolved
 oxygen and nutrients positively affect functional diversification.
 Moreover, the functional diversity patterns suggest low resilience of
 high-latitude coastal ecosystems, which are heavily exploited and
 threatened by climate change, hence highlighting the urgent need for
 effective conservation policies.
description: Based on the geographical
 ranges of 2350 species of marine macroinvertebrates belonging to seven
 phyla found in coastal shallow ecosystems, we built a presence-absence
 matrix with one latitudinal degree resolution. The dataset was compiled
 using records obtained from specialized literature, scientific expeditions
 (SARCE 2013, CONAMA 2008), data platforms (Ocean Biogeographic Information
 System, OBIS, and Global Biodiversity Information Facility, GBIF) and
 museum collections. Species known to occur only in waters deeper than 30m
 were not included. Further taxonomic detail can be found in Appendix 2.
 Given that the only source of variation is the presence or absence of
 species by latitudinal band, we grouped the species in functional entities
 (FEs) before the calculation of the FD indices, implementing the species
 number per FE as a proxy of abundance. Each species was assigned to one of 247 FEs
 based on 31 ecological traits nested into eight ecological attributes
 (Table S2). Trait selection was based on the potential to reflect
 ecological processes, for instance, body size and morphology, which have
 been related to vulnerability to disturbance and ecological roles, and
 that could readily and unequivocally be assigned to species for which
 there are no ecological studies. Trait compilation was based on
 information from online repositories, scientific journals and specialist
 texts. The chosen traits maintain the balance between redundancy and
 uniqueness in order to diminish under- or over-estimation of functional
 attributes. To capture variation and trait affinity of a given taxon, a
 fuzzy coding technique was implemented to codify in categorical terms each
 species (or assign to one trait combination) depending on the presence and
 level of the trait (see Table S2). The relative abundances of FE were
 calculated as the number of species in a given trait combination, divided
 by the total richness recorded by latitudinal bin. Finally, a FE ×
 latitude matrix was generated to quantify the functional changes along the
 SEPC.
description: The database can be seen through Excel, and codes for each
 analysis can be run through the R program. 
description: # Functional biogeography of coastal marine invertebrates along the
 south-eastern Pacific coast **Author information** 1. Principal
 Investigator, Contact Information Name: Luis Felipe Opazo, Institution:
 Pontificia Universidad Catolica de Chile Address: Alameda 340, Santiago,
 Chile Email: [lfopazo@bio.puc.cl](mailto:lfopazo@bio.puc.cl) 2. Associate
 or Co-investigator, Contact Information Name: David L. Herrera,
 Institution: Pontificia Universidad Catolica de Chile Address: Alameda
 340, Santiago, Chile Email: [doherrera@uc.cl](mailto:doherrera@uc.cl) 3.
 Associate or Co-investigator, Contact Information Name: Fabio Labra,
 Institution: Universidad Santo Tomas Address: Av. Ejército Libertador 146,
 Santiago, Chile Email: [flabra@santotomas.cl](mailto:flabra@santotomas.cl)
 4. Associate or Co-investigator, Contact Information Name: Simon Castillo,
 Institution: Pontificia Universidad Catolica de Chile Address: Alameda
 340, Santiago, Chile Email: [scb.139@gmail.com](mailto:scb.139@gmail.com)
 5. Associate or Co-investigator, Contact Information Name: Sergio
 Navarrete, Institution: Pontificia Universidad Catolica de Chile Address:
 Alameda 340, Santiago, Chile Email:
 [snavarrete@bio.puc.cl](mailto:snavarrete@bio.puc.cl) *** # **Abstract**
 Characterizing the spatial structure of taxonomic and functional diversity
 (FD) of marine organisms across regional and latitudinal scales is
 essential for improving our understanding of the processes driving species
 richness and those that may constrain or enhance the set of species traits
 that define the functional structure of communities. Here, we present the
 functional diversity of coastal invertebrate macrofaunal species along the
 south-eastern Pacific (from 7°N to 56°S), describe spatial variation of
 species traits, and examine the relationship with environmental variables.
 For that, we defined the functional traits and the distribution ranges of
 2350 marine macroinvertebrates. Species traits are used calculated eight
 metrics of FD. Random forest regression was applied to identify
 significant relationships between FD and six environmental variables.
 Finally, functional b-turnover was estimated to detect alongshore shifts
 in functional structure and their coincidence with biogeographical
 domains. Our results show, in contrast with taxonomic richness, measures
 of trait differences, functional space and functional specialisation
 increase with latitude, while functional evenness exhibits a humpback
 shape, peaking at mid latitudes. Functional redundancy decreased
 significantly poleward, while indicatior of vulnerability increase. In
 contrast to taxonomic richness, FD was tightly connected to variables
 indicative of stress and productivity, such as dissolved oxygen and
 nutrients. Sea surface temperature and coastal area best explained the
 increased FD redundancy towards the tropics. The high spatial correlation
 between taxonomic and functional turnover suggests environmental filters
 play an important role in the functional structure of the seascape. Our
 findings suggest that processes favouring taxonomic richness are
 latitudinally divergent from those favouring functional diversity.
 Correlations with environmental variables suggest that increased sea
 surface temperature and measures of stability increase redundancy, while
 variations in dissolved oxygen and nutrients positively affect functional
 diversification. Moreover, the functional diversity patterns suggest low
 resilience of high latitude coastal ecosystems, which are heavily
 exploited and threatened by climate change, hence highlighting the urgent
 need for effective conservation policies. Keywords: South-eastern Pacific
 coast, marine invertebrates, multi-taxa, functional diversity, functional
 biogeography, functional richness, functional redundancy, b-functional
 diversity, environmental drivers, biogeographical regions. *** #
 Description of the dataset and file structure Reference and sources of the
 taxonomic and functional information of each species is described in
 detail at the dataset (DataSet.AppendixS2). The excel file is constituted
 of four sheets. *** ## DATA-SPECIFIC INFORMATION FOR: Sheet 1(Dataset).
 Number of columns: 99 (from column A to column CU), Number of rows: 2350
 (from row 2 to row 2351). * Columns “A” to column “F”, contains the
 taxonomic information hierarchical order for each describe species,
 (Phyllum, Class, Order, Family, Genus, and Species). This structure
 adequately captures the whole taxonomic variation across species. * Column
 “G”, contains the ecocode, which summarized the trait combination that
 defines the functional entity (FE) of each species given their strategy
 and ecological characteristics. ### **Ecological traits** * **Tiering
 strategy**: Is described by four columns (from “H” to “K”), and represent
 the relationship of the organism with the substrate or the interface
 sediment - water column. This strategy is subdivide in four subcategories:
 (1) Pelagic, (2) Erect, (3) Surface, and (4) Infaunal. * **Motility Level
 :** Is described by six columns (from“L” to “Q”), and indicates the
 velocity, the organism's ability to dispersal and the spatial
 extension of their activity ranges. This strategy is subdivide in six
 subcategories: (1) Freely-Fast, (2) Freely-Slow, (3) Fac.Unattached, (4)
 Fac.Attached, (5) NM.Unattached, and (6) NM.Attached. * **Feeding
 mechanisms**: Is described by six columns (from "R" to
 "W"), and reflects the energy flow and biomass across trophic
 levels. This strategy is subdivide in six subcategories: (1) Suspension,
 (2) Surf.Deposit, (3) Grazing, (4) Predatory, (5) Omnivory, and (6)
 Chemotrophic. * **Life type:** Is described by three columns (from “X” to
 “Z”) and indicate whether organism exhibits inter or intraspecific
 associations, for instance; (1) Colonial, (2) Solitary, and (3) Symbiont.
 * **Reproductive type:** Traits are listed from “AA” to “AB” and describe
 the number of reproductive events of an organism over its life cycle. This
 strategy is subdivide in two subcategories:(1) Semelparity and (2)
 Iteroparity. * **Type of Larval:** Is described by three columns (from
 “AC” to “AE), and indicates how the early developmental stages of marine
 invertebrate influences the length (time) of the life span in the water
 column. This strategy is subdivide in three subcategories:(1)
 Planktotrophic, (2) Lecithotrophic, and (3) Direct. * **Survival
 Strategy:** Is described by three columns (from “AF” to “AH”), and
 describe the behavior or physiological strategies developed to resist
 and/or avoid predation. This strategy is subdivide in three
 subcategories:(1) Escape/Hold-on, (2) Figth, and (3) Chemical/Mechanic. *
 **Body Size:** Is described by four columns (from “AI” to “AL”), and
 indicates the fraction of biomass that is transformed in the ecosystem. Is
 a fundamental trait because provides information about the contribution of
 species in terms of production, transformation, and incorporation of
 energy directly into the ecosystem. This strategy is subdivide in four
 subcategories: (1) &lt;10mm, (2) 10-50mm, (3) 50-100mm, and (4)
 &gt;100mm. Missing data: n/a (not aplicable) *** ### **Distribution
 ranges** Is an absence-presence matrix (from “AM” to “CU” columns), which
 represent the occurrence of each species along the south-eastern Pacific
 coast spanning from 7°N to 53°S. Each column shows the occurrence of all
 species for that specific latitud (or column). Latitudes were labeled as
 following: (LAT 7°N, LAT 6°N, LAT 5°N, LAT 4°N, LAT 3°N, LAT 2°N, LAT 1°N,
 LAT 0°, LAT 1°S, LAT 2°S, LAT 3°S, LAT 4°S, LAT 5°S, LAT 6°S, LAT 7°S, LAT
 8°S, LAT 9°S, LAT 10°S, LAT 11°S, LAT 12°S, LAT 13°S, LAT 14°S, LAT 15°S,
 LAT 16°S, LAT 17°S, LAT 18°S, LAT 19°S, LAT 20°S, LAT 21°S, LAT 22°S, LAT
 23°S, LAT 24°S, LAT 25°S, LAT 26°S, LAT 27°S, LAT 28°S, LAT 29°S, LAT
 30°S, LAT 31°S, LAT 32°S, LAT 33°S, LAT 34°S, LAT 35°S, LAT 36°S, LAT
 37°S, LAT 38°S, LAT 39°S, LAT 40°S, LAT 41°S, LAT 42°S, LAT 43°S, LAT
 44°S, LAT 45°S, LAT 46°S, LAT 47°S, LAT 48°S, LAT 49°S, LAT 50°S, LAT
 51°S, LAT 52°S, and LAT 53°S). Missing data: n/a (not aplicable) *** ##
 DATA-SPECIFIC INFORMATION FOR: Sheet 2 (Taxonomic Ref) Number of columns:
 12 (from column A to column L), Number of rows: 2350 (from row 2 to row
 2351). ### **Taxonomic References** Indicates the references and source of
 the taxonomic and geographical information used in this study. 1. This
 matrix features six columns arranged in a hierarchical sequence, spanning
 from column "A" to column "F". These columns
 correspond to distinct taxonomic ranks, progressing from phylum to
 species, encompassing Class, Order, Family, and Genus along the way
 (Phyllum, Class, Order, Family, Genus, and Species). 2. The information
 about the taxonomist who described each species can be found in column
 “G”, where the reference authorship is documented (Specie Ref). 3. In five
 columns from “H” to “L”, contains the bibliographic references validating
 the information regarding the geographic distribution ranges of each
 species have been seamlessly incorporated. In certain instances, the
 distribution data has been corroborated through multiple references to
 uphold the veracity and dependability of the information (Ref 1, Ref 2,
 Ref 3, Ref 4, Ref 5). 4. Missing data: n/a (not aplicable) *** ##
 DATA-SPECIFIC INFORMATION FOR: Sheet 3(Functional Ref). Number of columns:
 32 (from column A to column AK), Number of rows: 2350 (from row 2 to row
 2351). ### Functional References Indicates the references and source of
 the taxonomic and functional description used in this study. * This matrix
 features six columns arranged in a hierarchical sequence, spanning from
 column "A" to column "F". These columns correspond to
 distinct taxonomic ranks, e.g.: Phyllum, Class, Order, Family, Genus, and
 Species. * This matrix spans from column “G” to column “AK”, encompassing
 31 distinct functional traits to record the bibliographic references where
 the ecological information of the species is found. These traits have been
 meticulously grouped into eight overarching functional categories (Tiering
 (T), Motility (M), Feeding Mechanisms (F), Life Forms (LF), Reproduction
 (R), Larvae (L), Survival Strategy (SS), and Body Size (S)). Each
 functional category is defined by its initials to classify the functional
 trait that makes up each functional category. * Tiering (T), from “G” to
 “J”  (T1, T2,    T3, and T4). * Motility (M), from “K” to “P” (M1, M2, M3,
 M4, M5, and M6). * Feeding Mechanisms (F), from “Q” to “V” (F1, F2, F3,
 F4, F5, and F6). * Life Forms (LF), from “W” to “Y” (LF1, LF2, and LF3). *
 Reproduction (R), from “Z” to “AA” (R1, and R2). * Larvae (L), from “AB”
 to “AD” (L1, L2, and L3). * Survival Strategy (SS), from “AE” to “AG”
 (SS1, SS2, and SS3). * Body Size (S), from “AH” to “AK” (S1, S2, S3, and
 S4). From column “G” to “AK”, the cells are filled by references relating
 to the functional traits for each species. These references are
 systematically organized based on their functional categories, with the
 information for a single trait per species. Cells filled by
 "n/a," indicates no information available. *** ## DATA-SPECIFIC
 INFORMATION FOR: Sheet 4 (environmental drivers). Number of columns: 17
 (from column A to column Q), Number of rows: 61 (from row 2 to row 62).
 ### **Environmental data** List of environmental variables considered in
 the Random Forest model. * From columns A to D indicates: Lat: Latitude in
 degree, Long: Longitude in degree, Latitude: Latitudinal bin, Longitude:
 Longitudinal bin. * The column “E” (Habitat): Contains habitat information
 according the species substrate. * Columns “F” to “Q”, reports the mean
 value by latitude (from 7°N to 53°S) of each envionmental variable
 (Driver). Mean value of each variable represent the monthly average by
 latitude from 2014–2020. Area represents the exposed coastline to 200 m
 depth and extracted from The General Bathymetric Chart of the Oceans
 (GEBCO 2020) using Quantum GIS software. All metrics were estimated by one
 degree of spatial resolution. * Area\_GEBCO, (Shelf area,
 km<sup>2</sup>) * log(Area) * Subsurface temperature (SST, °C)
 * log10\_SST * Dissolved Oxygen (DO, mol × m<sup>–3</sup>) *
 log10\_DO * Chl\_mean (Chl, mol m<sup>–3</sup> x
 day<sup>–1</sup>) * log10\_Chl * Carbon (Organic Carbon , g
 m<sup>–3</sup> × day<sup>−1</sup>) * log10\_Carbon
 * Nitrates (NO<sub>3</sub>, mol m<sup>–3</sup>) *
 log10\_Nitrate Missing data: n/a (not aplicable) *** ## Sharing/Access
 information 1. Licenses/restrictions placed on the data: CC0 1.0 Universal
 (CC0 1.0) Public Domain 2. Links to publications that cite or use the
 data: 3. Links to other publicly accessible locations of the data:
 Alternatively, dataset can be download throughout Dropbox. However, for
 it, require to contact to L. Felipe Opazo (Corresponding author) by email:
 lfopazo@bio.puc.cl 4. Links/relationships to ancillary data sets: None 5.
 Was data derived from another source? Yes The dataset was compiled using
 records obtained from specialized literature, scientific expeditions
 (SARCE 2013, CONAMA 2008), data platforms (Ocean Biogeographic Information
 System, OBIS, and Global Biodiversity Information Facility, GBIF) and
 museum collections. Reference and sources of the taxonomic and functional
 information of each species is described in detail at the dataset. ####
 Recommended citation for this dataset: Opazo Mella, Luis-Felipe et al.
 (2023), Functional biogeography of coastal marine invertebrates along the
 south-eastern Pacific coast, Dryad, Dataset,
 [https://doi.org/10.5061/dryad.1ns1rn90n](https://doi.org/10.5061/dryad.1ns1rn90n) #### **Data was derived from the following sources:** SARCE 2013, CONAMA 2008, data platforms (Ocean Biogeographic Information System, OBIS, and Global Biodiversity Information Facility, GBIF) and museum collections. ## Code/Software Code in R program are provided to calculate functional diversity facets, Random Forest model and jackknife analysis: each routine is packaged separately and each one contains its own input data set ready to run. Any question does not hesitate to contact the corresponding Author.