In monteroserra/rOceansNew: Spatial Trends in Marine Biodiversity and Conservation

rOceans

An R Package for exploring spatial trends in marine biodiversity and conservation. Here have examples of functions to access and explore spatial patterns of marine biodiversity. The functions are separated in set #1 for single or few species analyses and set #2 for analyzing large data sets downloaded from OBIS or GBIF.

*Currently, working on further set of functions to access and explore spatial data on climate change and other stressors as well as MPAs coverage.

(1) Download rOceansNew from GitHub and load the package

invisible(devtools::install_github("monteroserra/rOceansNew"))
invisible(library(rOceansNew))

Functions for accessing, merging and cleaning geographich occurrences data

Function #1 oceanDivDat

Allows accessing occurrence data from GBIF and OBIS for a given vector of species names. It also allows merging data from these two sources and removing duplicates as well as land-based occurrences.

# Define target species names: example with three corals
coral_species = c("Corallium rubrum", "Acropora palmata", "Paramuricea clavata")

# Get, merge and filter occurrences from GBIF & OBIS using oceanDivDat()
corals = oceanDivDat(species_names = coral_species,
                     data_source = "OBIS_&_GBIF", 
                     remove_duplicates = T, 
                     remove_land_dots = T)

Compute abundance per grid cell and visualize the results using oceanAbunGrid() & oceanMaps()

#Compute abundance per grid cell

corals_abundance = oceanAbundGrid(corals)

oceanMaps(grid = corals_abundance, main = "Abundance of three coral species")

Function #2 oceanDataCheck

Allows merging, checking, filtering the raw data from OBIS and/or GBIF into a common standard database. It removes occurrences with inconsistent or inconmplete taxonomic information, land-based occurrences, and duplicate occurrences.

For this set of functions, we first need to access and download data from GBIF (www.gbif.org) and OBIS (www.iobis.org).

Here I show an example with data of species within the genus Acropora that were directly downloaded from the website on June 2018. The data can be accessed from my GitHub repository "monteroserra/rOceanData" or using data()

#Accesing raw datasets
data(Acropora_GBIF) 
data(Acropora_OBIS)

#Checking OBIS or GBIF individually
Acropora_OBIS_checked = oceanDataCheck(OBIS_occurrences = Acropora_OBIS, source = "OBIS")

Acropora_GBIF_checked = oceanDataCheck(GBIF_occurrences = Acropora_GBIF, source = "GBIF")

Merging OBIS and GBIF Datasets, and checking for taxonomic and geographic issues

Acropora_Total_Checked = oceanDataCheck(GBIF_occurrences = Acropora_GBIF,
                                        OBIS_occurrences = Acropora_OBIS,
                                        source = "GBIF_&_OBIS")

Functions for analyzing and visualizing spatial trends in marine biodiversity

Function #3 oceanAbundGrid

Allows creating global or delimited spatial grids with abundance of occurrences per cell at differenc cell sizes.

Following with the Acropora example, we will use this function to compute global rasters of abundance per grid cell for Acropora species at different resolutions (10 x 10?) (5 x 5?) (1? x 1?) and (0.5 x 0.0?) using cell_size

Acropora_abundance_10x10 = oceanAbundGrid(occurrences = Acropora_Total_Checked, cell_size=10)
Acropora_abundance_5x5 = oceanAbundGrid(occurrences = Acropora_Total_Checked, cell_size=5)
Acropora_abundance_1x1 = oceanAbundGrid(occurrences = Acropora_Total_Checked, cell_size=1)
Acropora_abundance_0.5x0.5 = oceanAbundGrid(occurrences = Acropora_Total_Checked, cell_size=0.5)

Function #3 oceanMaps

A versatile data mapping tool for a rapid visualization of marine biodiversity patterns previously computed.

par(mfrow=c(2,2))
oceanMaps(Acropora_abundance_10x10, logScale=T, main="Acropora abundance (10x10)")
oceanMaps(Acropora_abundance_5x5, logScale=T, main="Acropora abundance (5x5)")
oceanMaps(Acropora_abundance_1x1, logScale=T, main="Acropora abundance (1x1)")
oceanMaps(Acropora_abundance_0.5x0.5, logScale=T, main="Acropora abundance (0.5x0.5)")
par(mfrow=c(1,1))

Function #4 oceanDiversity

Allows computing several diversity metrics (shannon, richness, simpson) at global scale or for specifically delimited areas. It also provides a species abundance matrix per grid cell, to allow for further multivariate analyses.

Computing global rasters of diversity metrics: example of Acropora species

Acropora_div = oceanDiversity(occurrences = Acropora_Total_Checked)

Acropora_species_matrix = Acropora_div[[1]]
Acropora_richness = Acropora_div[[2]]
Acropora_simpson = Acropora_div[[3]]
Acropora_shannon = Acropora_div[[4]]

par(mfrow=c(2,2))
oceanMaps(Acropora_richness, logScale=T,main="Acropora Species Richness")
oceanMaps(Acropora_shannon, logScale=T,main="Acropora Shannon Diversity")
oceanMaps(Acropora_simpson, logScale=T,main="Acropora Simpson Diversity")
par(mfrow=c(1,1))

Function #5 oceanHotspots

This function classifies sites into high, mid, and low diversity given any biodiversity metric

AcroporaHotspots = oceanHotspots(biodiversity_grid = Acropora_richness)

Functions for accessing and exploring environmental drivers of marine biodiversity

Function #6 oceanEnv

Allows accessing environmental layers at present conditions to explore and test potential drivers of marine biodiversity patterns

Following with the previous example on abundance and diversity of Acropora species

Acropora_abundance = oceanAbundGrid(occurrences = Acropora_Total_Checked, cell_size=5)

# getting envioronmental parameters and linking them to biodiversity patterns
abundance_environ_df = invisible(oceanEnv(biod_grid = Acropora_abundance,
                       biodiv_metric = "abundance", 
                       plot=T, 
                       log_scale=T))

We can also explore environmental drivers of other metrics such as species richness using previously computed grids

abundance_environ_df = invisible(oceanEnv(biod_grid = Acropora_richness,
                       biodiv_metric = "richness", 
                       plot=T, 
                       log_scale=T))

We can specify any alternative environmental parameters using env_parameters to see the full list of avilable parameters (326)

env_variables = sdmpredictors::list_layers()
marine_layers = env_variables[env_variables$marine==T, ]

Function #7 oceanFuture

Allows accessing, analyzing and mapping future trends in temeperature and other environmental parameters according to different IPCC scenarios

In the following example, we will access and map expected increases in Sea Surface Temperature data for 2100 according to different IPCC scenarios

SST_2100_RCP8.5 = oceanFuture(IPCC_scenario = "RCP85")
SST_2100_RCP4.5 = oceanFuture(IPCC_scenario = "RCP45")
SST_2100_RCP2.5 = oceanFuture(IPCC_scenario = "RCP26")

You can check other available enviornmental parameters with avaialable future projections

env_variables_future = sdmpredictors::list_layers_future()

Other examples of potentially useful environmental drivers expected to change

#Maximum Surface Salinity
Surface_Salinity_max_2100_RCP8.5 = oceanFuture(env_parameter = "BO2_salinitymax_ss",
                                               IPCC_scenario = "RCP45")
#IPCC RCP4.5
Surface_Salinity_max_2100_RCP2.5 = oceanFuture(env_parameter = "BO2_salinitymax_ss", 
                                               IPCC_scenario = "RCP85")
#Ice thick
Ice_thick_trends_2100_RCP8.5 = oceanFuture(env_parameter = "BO2_icethickmax_ss",
                                           IPCC_scenario = "RCP45")

Function #8 oceanVulnerab

Allows accessing future warming trends and linking them to global hotspots of high biodiversity

In this example, we analyze and plot hotspots of Acropora biodiversity and the expected warming trends according to three IPPC scenarios.

hotspots_vulnerb <- oceanVulnerab(biodiversity_grid = AcroporaHotspots,
                    map_climtatic_impacts = F, plot_histograms = T)

Second example, exploring shark biodiversity trends of the genus Carharhinidae

Compute a abundance grid

data(Carcharhinidae_total_checked)

Carcharhinidae_abundance_10 = oceanAbundGrid(occurrences = Carcharhinidae_total_checked, cell_size=10)

Carcharhinidae_abundance_5 = oceanAbundGrid(occurrences = Carcharhinidae_total_checked, cell_size=5)

Carcharhinidae_abundance_1 = oceanAbundGrid(occurrences = Carcharhinidae_total_checked, cell_size=1)

Carcharhinidae_abundance_0.5 = oceanAbundGrid(occurrences = Carcharhinidae_total_checked, cell_size=0.5)

Visualize abundance

par(mfrow=c(2,2))
oceanMaps(Carcharhinidae_abundance_10, logScale=T, main="Carcharhinidae abundance (10x10)")
oceanMaps(Carcharhinidae_abundance_5, logScale=T, main="Carcharhinidae abundance (5x5)")
oceanMaps(Carcharhinidae_abundance_1, logScale=T, main="Carcharhinidae abundance (1x1)")
oceanMaps(Carcharhinidae_abundance_0.5, logScale=T, main="Carcharhinidae abundance (0.5x0.5)")
par(mfrow=c(1,1))

Exploring envioronmental drivers of abundance patterns

abundance_environ_df = invisible(oceanEnv(biod_grid = Carcharhinidae_abundance_1,
                                biodiv_metric = "abundance", 
                                plot=T, 
                                log_scale=T))

Computing species abundance matrix - and several diversity metrics

Carcharhinidae_diversity = oceanDiversity(occurrences = Carcharhinidae_total_checked, print=F)

# Visualizing diversity metrics
par(mfrow=c(2,2))
oceanMaps(Carcharhinidae_diversity[[2]], main = "Carcharhinidae richness")
oceanMaps(Carcharhinidae_diversity[[3]], main = "Carcharhinidae Shannon Diversity")
oceanMaps(Carcharhinidae_diversity[[4]], main = "Carcharhinidae Simpson Diversity")
par(mfrow=c(1,1))

Exploring envioronmental drivers of abundance patterns

diversity_env_df = invisible(oceanEnv(biod_grid = Carcharhinidae_diversity[[2]],
                                biodiv_metric = "richness", 
                                plot=T, 
                                log_scale=T))

Exploring biodiversity hotspots and vulnerability of these hotspots to warming

Carcharhinidea_hotspots = oceanHotspots(biodiversity_grid = Carcharhinidae_diversity[[2]], 
                          hotspot_map=T, main = "Hotspots and coldspots of shark diversity")


hotspots_vulnerb <- oceanVulnerab(biodiversity_grid = Carcharhinidea_hotspots,
                     map_climtatic_impacts = F, plot_histograms = T)

monteroserra/rOceansNew documentation built on May 13, 2019, 1:09 p.m.