Archiv der Kategorie: Papers

New article out comparing transplant, OTCs and gradients

Transplants, Open Top Chambers (OTCs) and Gradient Studies Ask Different Questions in Climate Change Effects Studies

Long-term monitoring, space-for-time substitutions along gradients, and in situ temperature manipulations are common approaches to understand effects of climate change on alpine and arctic plant communities. Although general patterns emerge from studies using different approaches, there are also some inconsistencies. To provide better estimates of plant community responses to future warming across a range of environments, there have been repeated calls for integrating different approaches within single studies. Thus, to examine how different methods in climate change effect studies may ask different questions, we combined three climate warming approaches in a single study in the Hengduan Mountains of southwestern China. We monitored plant communities along an elevation gradient using the space-for-time approach, and conducted warming experiments using open top chambers (OTCs) and plant community transplantation toward warmer climates along the same gradient. Plant species richness and abundances were monitored over 5 years addressing two questions: (1) how do plant communities respond to the different climate warming approaches? (2) how can the combined approaches improve predictions of plant community responses to climate change? The general trend across all three approaches was decreased species richness with climate warming at low elevations. This suggests increased competition from immigrating lowland species, and/or from the species already growing inside the plots, as indicated by increased biomass, vegetation height or proportion of graminoids. At the coldest sites, species richness decreased in OTCs and along the gradient, but increased in the transplants, suggesting that plant communities in colder climates are more open to invasion from lowland species, with slow species loss. This was only detected in the transplants, showing that different approaches, may yield different results. Whereas OTCs may constrain immigration of new species, transplanted communities are rapidly exposed to new neighbors that can easily colonize the small plots. Thus, different approaches ask slightly different questions, in particular regarding indirect climate change effects, such as biotic interactions. To better understand both direct and indirect effects of climate change on plant communities, we need to combine approaches in future studies, and if novel interactions are of particular interest, transplants may be a better approach than OTCs.

Code based author and affiliation list

“Everything is possible in R”

This might not be an everyday problem, but doing this task by hand would take forever. And when finished, I would have to start all over again, because of a tiny litte change. I realized, R code is the only solution!

Here is the problem: I am writing a paper with 109 authors. This is a challenging task in itself. But a couple of days ago I realized writing the author list and their affiliations, arranged by the authors last name and numbered affiliations would be a very tedious task. And as soon as it was done, one of the author would tell me about a new affiliation and another one that this affiliation was old and so on. It did not need a lot of persuasion before I opened R and started to type.

Lets assume we have three authors (we keep it simple for now). We will also need to load the tidyverse library, which is not shown here.

# Make a data frame with 4 columns
dat <- data.frame(FirstName = c("Harry James", "Fleur", "Viktor"),
           LastName = c("Potter", "Delacour", "Krum"),
           Affiliation1 = c("Hogwarts School of Witchcraft and Wizardry, UK", "Beauxbatons Academy of Magic, France", "Durmstrang Institute for Macigal Learning, Russia"),
           Affiliation2 = c(NA, "Hogwarts School of Witchcraft and Wizardry, UK", "Hogwarts School of Witchcraft and Wizardry, UK"))
dat 
##     FirstName LastName                                      Affiliation1
## 1 Harry James   Potter    Hogwarts School of Witchcraft and Wizardry, UK
## 2       Fleur Delacour              Beauxbatons Academy of Magic, France
## 3      Viktor     Krum Durmstrang Institute for Macigal Learning, Russia
##                                     Affiliation2
## 1                                           <NA>
## 2 Hogwarts School of Witchcraft and Wizardry, UK
## 3 Hogwarts School of Witchcraft and Wizardry, UK

The next step is to prepare the table for what we want to do. Here you can rename columns, filter the table, rearange it etc. For this table we only want to merge the 2 columns containing the affiliations into a single column. We will use “gather” for this.

# Prepare data
dat <- dat %>% 
  # gather all affiliations in one column
  gather(key = Number, value = Affiliation, Affiliation1,  Affiliation2) %>%
  # remove rows with no Affiliations
  filter(!is.na(Affiliation))

Then we need to do the following:

  • Arrange the column by last name
  • Extract the initials and add a dot at the end of each letter
  • Add a column ID to the data frame from 1 to n
  • Then we replace the ID in rows with the same affiliation with the lowest ID number
  • The previous step might have left some gaps in that we could have ID 1, 3 and 4. So the next step is to change the IDs to 1, 2 and 3. For this we use the little function rankID
  • Finally, we paste the last and initials
# Function to get affiliations ranked from 1 to n (this function was found on Stack Overflow)
rankID <- function(x){
  su=sort(unique(x))
  for (i in 1:length(su)) x[x==su[i]] = i
  return(x)
}


NameAffList <- dat %>% 
  arrange(LastName, Affiliation) %>% 
  rowwise() %>% 
  # extract the first letter of each first name and put a dot after each letter
  mutate(
    Initials = paste(stringi::stri_extract_all(regex = "\\b([[:alpha:]])", str = FirstName, simplify = TRUE), collapse = ". "),
    Initials = paste0(Initials, ".")) %>%
  ungroup() %>% 
  # add a column from 1 to n
  mutate(ID = 1:n()) %>%
  group_by(Affiliation) %>% 
  # replace ID with min number (same affiliations become the same number)
  mutate(ID = min(ID)) %>% 
  ungroup() %>% 
  # use function above to assign new ID from 1 to n
  mutate(ID = rankID(ID)) %>%
  #Paste Last and Initials
  mutate(name = paste0(LastName, ", ", Initials)) 

The last thing we need to do is to print a list with all the names + IDs and one with all the affiliations + IDs.

# Create a list with all names
NameAffList %>%   
  group_by(LastName, name) %>% 
  summarise(affs = paste(ID, collapse = ",")) %>% 
  mutate(
    affs = paste0("^", affs, "^"),
    nameID = paste0(name, affs)     
         ) %>% 
  pull(nameID) %>% 
  paste(collapse = ", ")
## [1] "Delacour, F.^1,2^, Krum, V.^3,2^, Potter, H. J.^2^"
# Create a list with all Affiliations
NameAffList %>% 
  distinct(ID, Affiliation) %>% 
  arrange(ID) %>% 
  mutate(ID = paste0("^", ID, "^")) %>% 
  mutate(Affiliation2 = paste(ID, Affiliation, sep = "")) %>% 
  pull(Affiliation2) %>% 
  paste(collapse = ", ")
## [1] "^1^Beauxbatons Academy of Magic, France, ^2^Hogwarts School of Witchcraft and Wizardry, UK, ^3^Durmstrang Institute for Macigal Learning, Russia"

Et voilà! Names and affiliations:

Delacour, F.1,2 Krum, V.3,2 Potter, H. J.2

1Beauxbatons Academy of Magic, France, 2Hogwarts School of Witchcraft and Wizardry, UK, 3Durmstrang Institute for Macigal Learning, Russia

Here is one final trick! If this list is used in a paper, the IDs for the affiliations should be superscripts. This can of course be done manually, but again, with 109 authors… So, this is why I added the ^ before and after the numbers. If you copy the name and affiliation lists into an R markdown file and run it (or produce them directly in an R markdown file), the numbers will become superscript.

Thank you Richard Telford for helping with this code and generally stimulating conversations about coding.

The devil is in the detail

The devil is in the detail: Nonadditive and context‐dependent plant population responses to increasing temperature and precipitation

Plants marked with toothpicks for demographic study. Picture: Siri Lie Olsen

In climate change ecology, simplistic research approaches may yield unrealistically simplistic answers to often more complicated problems. In particular, the complexity of vegetation responses to global climate change begs a better understanding of the impacts of concomitant changes in several climatic drivers, how these impacts vary across different climatic contexts, and of the demographic processes underlying population changes. Using a replicated, factorial, whole‐community transplant experiment, we investigated regional variation in demographic responses of plant populations to increased temperature and/or precipitation. Across four perennial forb species and 12 sites, we found strong responses to both temperature and precipitation change. Changes in population growth rates were mainly due to changes in survival and clonality. In three of the four study species, the combined increase in temperature and precipitation reflected nonadditive, antagonistic interactions of the single climatic changes for population growth rate and survival, while the interactions were additive and synergistic for clonality. This disparity affects the persistence of genotypes, but also suggests that the mechanisms behind the responses of the vital rates differ. In addition, survival effects varied systematically with climatic context, with wetter and warmer + wetter transplants showing less positive or more negative responses at warmer sites. The detailed demographic approach yields important mechanistic insights into how concomitant changes in temperature and precipitation affect plants, which makes our results generalizable beyond the four study species. Our comprehensive study design illustrates the power of replicated field experiments in disentangling the complex relationships and patterns that govern climate change impacts across real‐world species and landscapes.

Picture on title page

My new paper on trait differentiation and adaptation along elevational gradients is out. And my picture is on the title page!

Abstract

Studies of genetic adaptation in plant populations along elevation gradients in mountains have a long history, but there has until now been neither a synthesis of how frequently plant populations exhibit adaptation to elevation nor an evaluation of how consistent underlying trait differences across species are. We reviewed studies of adaptation along elevation gradients (i) from a meta‐analysis of phenotypic differentiation of three traits (height, biomass and phenology) from plants growing in 70 common garden experiments; (ii) by testing elevation adaptation using three fitness proxies (survival, reproductive output and biomass) from 14 reciprocal transplant experiments; (iii) by qualitatively assessing information at the molecular level, from 10 genomewide surveys and candidate gene approaches. We found that plants originating from high elevations were generally shorter and produced less biomass, but phenology did not vary consistently. We found significant evidence for elevation adaptation in terms of survival and biomass, but not for reproductive output. Variation in phenotypic and fitness responses to elevation across species was not related to life history traits or to environmental conditions. Molecular studies, which have focussed mainly on loci related to plant physiology and phenology, also provide evidence for adaptation along elevation gradients. Together, these studies indicate that genetically based trait differentiation and adaptation to elevation are widespread in plants. We conclude that a better understanding of the mechanisms underlying adaptation, not only to elevation but also to environmental change, will require more studies combining the ecological and molecular approaches.

Working and Learning in a Field Excursion

A colleague has published an article on how biology students learn on field excursions. In addition, to study and learn about nature, these students were themselves being observed by Torstein Nielsen Hole, currently a PhD candidate at bioceed, university of Bergen.

For this study he joined a field course on Svalbard. It’s interesting to read about how biologists discuss their work in the field. And the article covers all from bare soil, death and guns (protection from polar).

Trait differentiation and adaptation of plants along elevation gradients

New review on trait differentiation and adaptation of plants along elevation gradients.

Studies of genetic adaptation in plant populations along elevation gradients in mountains have a long history, but there has until now been neither a synthesis of how frequently plant populations exhibit adaptation to elevation nor an evaluation of how consistent underlying trait differences across species are. We reviewed studies of adaptation along elevation gradients (i) from a meta‐analysis of phenotypic differentiation of three traits (height, biomass and phenology) from plants growing in 70 common garden experiments; (ii) by testing elevation adaptation using three fitness proxies (survival, reproductive output and biomass) from 14 reciprocal transplant experiments; (iii) by qualitatively assessing information at the molecular level, from 10 genomewide surveys and candidate gene approaches. We found that plants originating from high elevations were generally shorter and produced less biomass, but phenology did not vary consistently. We found significant evidence for elevation adaptation in terms of survival and biomass, but not for reproductive output. Variation in phenotypic and fitness responses to elevation across species was not related to life history traits or to environmental conditions. Molecular studies, which have focussed mainly on loci related to plant physiology and phenology, also provide evidence for adaptation along elevation gradients. Together, these studies indicate that genetically based trait differentiation and adaptation to elevation are widespread in plants. We conclude that a better understanding of the mechanisms underlying adaptation, not only to elevation but also to environmental change, will require more studies combining the ecological and molecular approaches.

Plastic and genetic responses to shifts in snowmelt time

Our new article on plastic and genetic responses to shifts in snowmelt time in Ranunculus acris has just been accepted in PPEES. I will present the study at the ESA in Portland in August.

Changes in both temperature and precipitation will affect snowmelt time at high elevation, thereby influencing plant reproduction and growth. Species can respond to changed climate with phenotypic plasticity or genetic adaptation, and these responses might vary at different levels of advanced and delayed snowmelt time. Here we mimicked future climate change projections for western Norway by transplanting individuals of Ranunculus acris towards warmer, wetter and warmer & wetter climates. And we replicated the experiment along regional-scale temperature and precipitation gradients. This setup resulted in both advanced (warmer and warmer & wetter transplants) and delayed (wetter transplants) snowmelt in the experimental sites. We recorded phenological development and growth over one growing season.

The reproductive phenology of the transplanted R. acris individuals was affected by both phenotypic plasticity and genetic differences between populations of different origins, while growth showed only plastic responses. Plants expressed high plasticity to both advanced and delayed snowmelt time by acceleration of the onset of buds, flowers and fruits. Only the plants from wet and high-elevation sites showed a small response to advanced SMT. The late snowmelt time these populations experience could potentially cause high selection pressure leading to more constrains in plasticity. When grown under common conditions, plants from late snowmelt sites responded with earlier onset of phenological development, suggesting that the timing of snowmelt exerts strong selection on reproduction. To project species fates under future climate we need to consider the interplay between genetic adaptation and plastic responses under different climate contexts, especially towards the species range limits.

Plasticity and genetic difference in growth (top) as the difference in leaf size +/- 2SE in cm (top) and first flowering in days after snowmelt timing (SMT; bottom) between treatment and origin-control (left) or destination-control (right) plants. The x-axis represents the difference in SMT between origin and destination site. The colours indicate the transplant treatments to: warmer (red), wetter (blue) and warmer & wetter (purple) climate. Points above/below the dashed grey line indicate larger/smaller leaf size or earlier/later days since SMT for first flowering in the transplanted plants compared to the origin-control (left) or destination-control (right) plants. Closed circles indicate a significant difference between treatment and destination-control plants and open circles no significant difference. Dashed error bars indicate a sample size lower than 6 individuals.

Delnevo, N., Petraglia, A., Carbognani, M., Vandvik, V. and Halbritter, A.H. (accepted). Plastic and genetic responses to shifts in snowmelt time affects the reproductive phenology and growth of Ranunculus acris. PPEES.

 

 

 

New article on mushroom-mimickring Dracula orchids and 3D printed flowers

Tobias Policha and collegues published an interestig article how they used 3D printed flowers to disentangle visual and olfactory signals in an orichid. You can get to the article here. The picture shows a Dracula lafleurii.

Dracula lafleuriicropped

Flowers use olfactory and visual signals to communicate with pollinators. Disentangling the relative contributions and potential synergies between signals remains a challenge. Under- standing the perceptual biases exploited by floral mimicry illuminates the evolution of these signals. Here, we disentangle the olfactory and visual components of Dracula lafleurii, which mimics mushrooms in size, shape, color and scent, and is pollinated by mushroom-associated flies.

 To decouple signals, we used three-dimensional printing to produce realistic artificial flower molds that were color matched and cast using scent-free surgical silicone, to which we could add scent. We used GC-MS to measure scents in co-occurring mushrooms, and related orchids, and used these scents in field experiments.

 By combining silicone flower parts with real floral organs, we created chimeras that identi- fied the mushroom-like labellum as a source of volatile attraction. In addition, we showed remarkable overlap in the volatile chemistry between D. lafleurii and co-occurring mush- rooms.

 The characters defining the genus Dracula – a mushroom-like, ‘gilled’ labellum and a showy, patterned calyx – enhance pollinator attraction by exploiting the visual and chemosen- sory perceptual biases of drosophilid flies. Our techniques for the manipulation of complex traits in a nonmodel system not conducive to gene silencing or selective breeding are useful for other systems.

Picture from: http://cascade.uoregon.edu/fall2010/natural-sciences/natures-masters-of-disguise/