R/LEEF_2_biomass_bemovi.R

In LEEF-UZH/LEEF.analysis: Access Functions, Tests and Basic Analysis of the RRD Data from the LEEF Project

#' LEEF-1 - Add biomass to traits
#'
#' @param ciliate_traits
# traits as read from file \code{morph_mvt_TIMESTAMP.rds}
#' @param ciliate_density density as read from file \code{mean_density_per_ml_TIMESTAMP.rds}
#'
#' @return list containing two objects, \code{traits} containing complete traits file
#'   as the argument \code{algai_traits} day includinc biomass column and \code{biomasses}
#'   per timestamp, bottle and species per milliliter.
#' @export
#'
#' @examples
LEEF_2_biomass_bemovi <- function(
    ciliate_traits,
    ciliate_density,
    extrapolation_factor,
    cropping_factor) {
    # mag 16
    ## extrapolation_factor <- 10.044
    ##  cropping.factor <- 1

    # mag 25 cropped
    ##   extrapolation_factor <- 23.367
    ##  cropping.factor <- 4

    # mag 25
    ##  extrapolation_factor_25 <- 23.367
    ##  cropping.factor <- 1

    video_biomass_species <- c(
        "Coleps_irchel", # species for which biomass is calculated
        "Colpidium",
        "Stylonychia2",
        "Paramecium_caudatum",
        "Paramecium_bursaria",
        "Euplotes",
        "Loxocephallus"
    )

    ciliate_traits_normalCases <- ciliate_traits %>%
        dplyr::filter(species %in% video_biomass_species) %>%
        mutate(
            height = ifelse(
                species %in% c("Euplotes", "Stylonychia2"),
                mean_minor / 3,
                ifelse(
                    species %in% c("Paramecium_bursaria"),
                    mean_minor / 1.5,
                    mean_minor
                )
            ),
            biomass = (4 / 3) * pi * (mean_minor / 2) * (height / 2) * (mean_major / 2), # calculate biomass
            biomass = biomass / 10^12 # change it from um3 to g, assuming water density
        )


    ciliate_traits_NotBiomass <- ciliate_traits %>%
        dplyr::filter(!(species %in% video_biomass_species)) %>%
        mutate(
            height = as.numeric(NA),
            biomass = as.numeric(NA)
        )

    # join datasets again

    ciliate_traits <- rbind(
        ciliate_traits_normalCases,
        ciliate_traits_NotBiomass
    ) # traits dataset finished here


    # calculate biomass per milliliter


    # Make sure, that we have n_frames and not N_frames
    names(ciliate_traits)[names(ciliate_traits) == "N_frames"] <- "n_frames"

    biomasses <- ciliate_traits %>%
        group_by(timestamp, bottle, species) %>%
        summarize(
          biomass = sum(biomass * n_frames, na.rm = TRUE) / (3 * 125) # if not 3 videos corrections is done below with dens_factor
        ) %>%
        mutate(
            biomass = biomass * extrapolation_factor * cropping_factor,
            biomass = ifelse(
                !(species %in% video_biomass_species),
                as.numeric(NA),
                biomass
            )
        ) # add biomass=NA if not a species


    biomasses$timestamp <- as.character(biomasses$timestamp)

    densities <- full_join(
        ciliate_density,
        biomasses,
        by = c("timestamp", "bottle", "species")
    )

    densities$biomass[densities$species %in% video_biomass_species & is.na(densities$biomass)] <- 0
    # mutate(
    #   biomass = case_when(
    #     species %in% flowcam_biomass_species & is.na(biomass) ~ 0,
    #     TRUE ~ biomass)
    # )

    biomasses$timestamp <- as.character(biomasses$timestamp)

    ## begin correct for excluded videos

    densities <- densities |>
        mutate(
            biomass = biomass * dens_factor
        )

    ## begin correct for excluded videos

    return(
        list(
            traits = ciliate_traits,
            density = unique(densities)
        )
    )
}