Surveys

2023-03-20: Kickoff Survey

Respondents & Scoring

Respondents

n_respondents: 5

Emails:

adriano.lima@aircentre.org
chris.kelble@noaa.gov
enrique.montes@noaa.gov
jeffrey.runge@maine.edu
n_anonymous: 1

Scoring

3: Critical
2: Important
1: Desirable
-1: Least Interesting

Note: blanks are not counted in average.

What other indicators or details of above that are important to consider?

chris.kelble@noaa.gov
What we should focus o is filling the gap in having a consistent, authoritative biodiversity indicator or suite of indicators.
jeffrey.runge@maine.edu
Associated with trophic structure: trophic linkage

Regions of interest, ie spatial analytical units to generate time series of indicators

What other regions or details of above that are important to consider?

enrique.montes@noaa.gov
Ecological Marine Units for that third dimension
anonymous
Wind Energy Areas

What other products or details of above that are important to consider?

chris.kelble@noaa.gov
We need to focus on the indicator time series before we focus on what the product will be.
jeffrey.runge@maine.edu

Synthesis products including modeling; continued research how to incorporate indicates into management decision making. This is still research. Not clear how any of the above products will meaningfully contribute to environmental decision making. 2) Participation of PIs and inclusion of MBON indicator data and synthesis in NOAA IEAs

Audience for whom are we generating these indicators and products

What other audience / users or details of above that are important to consider?

What other industries or details of above that are important to consider?

jeffrey.runge@maine.edu
Not clear how indicator data would be used by industry

What other initiatives or details of above that are important to consider?

chris.kelble@noaa.gov
America the Beautiful is just the US name for 30 x 2030. I would not split it out separately.

What other partners or details of above that are important to consider?

adriano.lima@aircentre.org
Protected Seas (protectedseas.net)
jeffrey.runge@maine.edu
IOOS regional associations and data servers
anonymous
IOOS Regional Associations

Observational and attribute data to use for developing indicators

What other data or details of above that are important to consider?

chris.kelble@noaa.gov
I would limit the data for indicator development to routine systematically collected data. Hodgepodge data where you have an observation here and there introduces a lot of biases that we would need to have a robust method to correct for.
jeffrey.runge@maine.edu
Abundance of key species contributing to trophic structure and function or to species with cultural value (e.g. whales)

Portals to distribute indicators

What other portals or details of above that are important to consider?

chris.kelble@noaa.gov
I think this depends on the indicator developed and its scale.
jeffrey.runge@maine.edu
IOOS regional associations, NOAA IEA

--- title: "Surveys" editor_options: chunk_output_type: console --- ## 2023-03-20: Kickoff Survey * [**Survey** of Priorities for Biodiversity Indicators MarineBON.org Working Group](https://docs.google.com/forms/d/e/1FAIpQLScUmZszSqUv2ER6lc8xFl-a8Lwn8OpA16LHoTHBYkcdWgQIIw/viewform) * [**Priorities** for MBON Biodiversity Indicators Working Group - Google Docs](https://docs.google.com/document/d/1Njq1BCFeByJEJUhvVC6PxcF20uKOrGWb8xNG2yh-vdI/edit#heading=h.1w03je9uy705) ```{r} librarian::shelf( dplyr, DT, glue, purrr, readr, stringr, tidyr) survey_csv <- "https://docs.google.com/spreadsheets/d/1xohI010hAwtbSyLpOr1-oFG5-W3ASiRC8EmHrDjNa0k/export?format=csv&gid=1947036924" d <- read_csv(survey_csv, col_types = "c") |> rename(email = `Your email? (optional)`) n_respondents <- nrow(d) emails <- d |> filter(!is.na(email)) |> arrange(email) |> pull(email) cols <- colnames(d) qs <- cols |> str_subset("\\[") |> str_split_i(" \\[", 1) |> unique() d_cols_other <- tibble( col = cols |> str_subset("What other"), question = col |> str_replace("\n"," ") |> str_replace(".*(What other .*)","\\1"), q = question |> str_replace("What other ([a-z]+) .*", "\\1") |> str_to_sentence()) col2q = setNames(d_cols_other$q, d_cols_other$col) d_other <- d |> select(all_of(c("email", d_cols_other$col))) |> pivot_longer( !email, names_to = "col", values_to = "comment", values_drop_na = T) |> mutate( q = recode(col, !!!col2q)) |> arrange(q, email) get_question <- function(q){ d_cols_other |> filter(q == !!q) |> pull(question) } get_comments <- function(q){ # DEBUG: q="General" d_other |> filter(q == !!q) |> select(respondent = email, comment) |> replace_na( list(respondent = "anonymous")) |> mutate( md = glue(" 1. {respondent} {comment}")) |> pull(md) |> paste(collapse = "\n") } summarize_q <- function(q, d){ # DEBUG: q = "Regions" d_q <- d |> select(starts_with(q)) |> pivot_longer( everything(), names_to = "option", values_to = "val_str", values_drop_na = T) |> mutate( option = str_replace( option |> str_replace("\n",""), ".*\\[(.*)\\]", "\\1"), val_int = recode( val_str, "1. Critical" = 3L, "2. Important" = 2L, "3. Desirable" = 1L, "4. Least Interesting" = -1L)) |> group_by(option) |> summarise( score_avg = mean(val_int)) |> arrange(desc(score_avg), option) } d_q <- tibble( q = qs |> str_split_i("\n", 1), data = map(q, summarize_q, d)) |> unnest(data) # setdiff(d_q$q, unique(d_other$q)) # Audience dt_q <- function(q){ d_q |> filter(q == !!q) |> select(Option = option, Score = score_avg) |> datatable( options = list(pageLength = 10)) |> formatRound(columns=c('Score'), digits=2) } ``` ::: {.callout-note collapse="true"} ## Respondents & Scoring ::::: {.columns} ::::::: {.column width="50%"} ### Respondents n_respondents: **`r n_respondents`** Emails: 1. `r paste(emails, collapse = "\n1. ")` 1. n_anonymous: `r n_respondents - length(emails)` ::::::: ::::::: {.column width="50%"} ### Scoring - `3`: Critical - `2`: Important - `1`: Desirable - `-1`: Least Interesting Note: blanks are not counted in average. ::::::: ::::: ::: ::: {.panel-tabset} ### Indicators ```{r} dt_q("Indicators") ``` `r get_question("Indicators")` `r get_comments("Indicators")` ### Regions Regions of interest, ie spatial analytical units to generate time series of indicators ```{r} dt_q("Regions") ``` `r get_question("Regions")` `r get_comments("Regions")` ### Products ```{r} dt_q("Products") ``` `r get_question("Products")` `r get_comments("Products")` ### Audience Audience for whom are we generating these indicators and products ```{r} dt_q("Audience") ``` `r get_question("Audience")` `r get_comments("Audience")` ### Industries ```{r} dt_q("Industries") ``` `r get_question("Industries")` `r get_comments("Industries")` ### Initiatives ```{r} dt_q("Initiatives") ``` `r get_question("Initiatives")` `r get_comments("Initiatives")` ### Partners ```{r} dt_q("Partners") ``` `r get_question("Partners")` `r get_comments("Partners")` ### Data Observational and attribute data to use for developing indicators ```{r} dt_q("Data") ``` `r get_question("Data")` `r get_comments("Data")` ### Portals Portals to distribute indicators ```{r} dt_q("Portals") ``` `r get_question("Portals")` `r get_comments("Portals")` :::