The network recruits PhD candidates into linked projects across a consortium of universities, research institutes, policy organisations, NGOs, and industry partners. Doctoral researchers get disciplinary training, cross-sector supervision, secondments to partner organisations, and a shared cohort experience across the whole network, rather than working in isolation at a single institution.

ReDiLEEP’s three interconnected goals are:

• developing standardised metrics for measuring response diversity across ecosystems;
• fostering collaboration between ecological research and policy implementation; and
• creating practical frameworks that put response diversity to work in conservation and restoration.

Response diversity — variation among species or organisms in how they respond to environmental change — is a big part of what keeps ecosystem functions stable when conditions shift. Helping turn that idea into something measurable and usable by policy- and decision-makers is exactly the kind of applied, cross-institutional organising this group is glad to be part of.

(Details of our specific role and doctoral projects within the consortium to be added.)

Response diversity — the idea that different species or organisms respond differently to the same environmental change — is increasingly framed as a sustainability strategy: it’s part of why ecosystem services can keep being delivered even as conditions shift, since not everything fails at once.

The network runs regular seminars with researchers presenting on biodiversity–stability relationships, species traits, and ecosystem resilience; maintains a shared Zotero library of response-diversity-related papers; organises symposia at conferences including GfO, BES, and ESJ; and sends out a quarterly newsletter. A steering committee has been in place since November 2022, with a new leadership team as of this year.

This group contributes to the network’s work on turning response diversity research into concrete opportunities for policy- and decision-makers — one strand of the broader push to make response diversity a genuinely useful concept outside academic ecology, not just inside it.

These are difficult questions we don’t have all the answers to. Here we give a few possibilities, based on the corpus of publications produced by a research programme aimed at creating added value by promoting integrative research. We visualise characteristics of that corpus and compare it to a reference corpus of publications from research that may or may not have been integrative. (The details of how we did this are below, for anyone who wants to try something similar.)

The example involves the corpus of publications arising from the University of Zurich Research Priority Programme in Global Change and Biodiversity (URPP GCB). The reference corpus is from related research fields at the University of Zurich over a similar time period (exact specification in the details section below).

This figure shows the tightly connected co-authorship network of 20 of the researchers in the URPP GCB — a great deal of integration in terms of collaboration. The number of co-authors per article in the URPP GCB corpus is about 20% greater than in the reference corpus, though only by around one author on average, and articles are cited about 30% more often (figure below).

## install the following if required
library(bibliometrix)
library(tidyverse)
library(circlize)
library(wordcloud2)
library(cowplot)

readFiles("~/Desktop/from_zotero.bib") %>%
  isibib2df() %>%
  dplyr::mutate(DOI = stringr::str_trim(DI)) %>%
  filter(!is.na(DOI)) %>%
  pull(DOI) %>%
  paste(collapse = " OR ") %>%
  cat(file = "~/Desktop/dois_for_WoS.txt")

focal_corpus <- readFiles("~/Desktop/focal_corpus_from_WoS.bib") %>%
  isibib2df() %>%
  mutate(TC = as.numeric(TC),
         Corpus = "URPPGCB",
         AUx = gsub(";", "", AU),
         num_authors = nchar(AU) - nchar(AUx))

D1 <- readFiles("~/Desktop/ref-corpus1.bib") %>% isibib2df()
D2 <- readFiles("~/Desktop/ref-corpus2.bib") %>% isibib2df()
D3 <- readFiles("~/Desktop/ref-corpus3.bib") %>% isibib2df()
D4 <- readFiles("~/Desktop/ref-corpus4.bib") %>% isibib2df()
D5 <- readFiles("~/Desktop/ref-corpus5.bib") %>% isibib2df()

reference_corpus <- rbind(D1, D2, D3, D4, D5) %>%
  mutate(TC = as.numeric(TC),
         Corpus = "Reference",
         AUx = gsub(";", "", AU),
         num_authors = nchar(AU) - nchar(AUx))
rm(D1, D2, D3, D4, D5)

focal_corpus_biban <- biblioAnalysis(focal_corpus)
NetMatrix <- biblioNetwork(focal_corpus, analysis = "collaboration", network = "authors", sep = ";")
MM <- as(NetMatrix, "matrix")
MM <- MM[order(rownames(MM)), order(colnames(MM))]
diag(MM) <- 0
top_authors <- focal_corpus_biban$Authors[1:20]
these_cols <- colnames(MM) %in% names(top_authors)
these_rows <- rownames(MM) %in% names(top_authors)
top_MM <- MM[these_rows, these_cols]
set.seed(999)
chordDiagram(top_MM, symmetric = TRUE)

reference_corpus <- select(reference_corpus, intersect(names(reference_corpus), names(focal_corpus)))
focal_corpus <- select(focal_corpus, intersect(names(reference_corpus), names(focal_corpus)))
corpus <- rbind(focal_corpus, reference_corpus)

summ_stats <- group_by(corpus, Corpus) %>%
  summarise(mean_cites = mean(TC),
            sem_cites = sd(TC) / sqrt(n()),
            mean_num_authors = mean(num_authors),
            sem_num_authors = sd(num_authors) / sqrt(n()))

p1 <- ggplot(summ_stats) +
  geom_point(mapping = aes(x = Corpus, y = mean_cites)) +
  geom_errorbar(mapping = aes(x = Corpus,
                               ymin = mean_cites - sem_cites,
                               ymax = mean_cites + sem_cites),
                width = 0.1) +
  ylab("Number of times cited")

p2 <- ggplot(summ_stats) +
  geom_point(mapping = aes(x = Corpus, y = mean_num_authors)) +
  geom_errorbar(mapping = aes(x = Corpus,
                               ymin = mean_num_authors - sem_num_authors,
                               ymax = mean_num_authors + sem_num_authors),
                width = 0.1) +
  ylab("Number of authors")

plot_grid(p2, p1)

The web application for valuing contributions in teams summarises the contributions a team can report in the associated Google Sheet. Below is a bit more detail about the problems it addresses and how.

The original invitation, sent round the Department of Evolutionary Biology and Environmental Studies (IEU) in late 2014, was simple:

Share your research experiences, subjects, highlights (even lowlights). Choose one or two of your best photos, have them printed, mounted, or framed as you like, and bring them along to be hung on boards at an apéro — around the Christmas party, or another department social. Maximum two photos per person.

It’s a nice format: everyone gets to see the fieldwork, systems, and places behind each other’s research, with no more organisational overhead than picking a date and finding some boards to hang things on. Worth repeating from time to time.