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The non-consumptive effects of academia

28/2/2023

By April Martinig

From the start of my academic journey, I have found myself at a crossroads. Research is supposed to be all consuming, and it is, if you let it be. My problem, or rather, the problem I am told by others to have, is that it is not my only passion.

I didn't quite grasp that this was an issue until a specific Wednesday during my PhD. Several hours into a lab meeting, I had to leave “early” to go to my varsity wrestling practice. I delayed going for as long as I could, but finally at 5 pm, already late, I had to go otherwise I would miss practice entirely. A lab-mate turned to me and very loudly said to the room how inappropriate it was that I was not prioritizing my graduate studies over all else. I still left the meeting, but their remark highlighted a sentiment[1] we don’t discourage often enough…

My lifestyle is not entirely different from pursuing a work-life balance; something many of us know, regardless of vocation, is already hard to maintain. Unlike what we might think of when hearing the latter, juggling two passions, fully and completely, means having very little time for life outside of them. But, without wrestling, I wouldn't be able to conduct research and without research, I wouldn't be the wrestler and coach I am today.

However, this dichotomy poses a fundamental issue as I pursue success in academia. As I continue to move up the academic ladder, the duality within me (i.e., being wildly passionate about something else in addition to research) does not seem to exist in “successful” academics. To be successful - to make it in the field - I find myself confronted with examples of researchers that work long hours and make sacrifices I deem too costly. This culture, which seems to continue to be glorified and perpetuated, is an example I won't buy into.

We shouldn't have to fight for space to be a balanced human. I can be a good academic and not make academia my whole life.

The crossroad is where I meet my passions, rather than follow them.

[1] As the old adage goes: one that chases two rabbits ends up with none.

Can ChatGPT do screening for a systematic review? Yes and more!!!

2/1/2023

by Shinichi Nakagawa

Before my Xmas break, I met ChatGPT (Generative Pre-trained Transformer). Since then, she has been my teacher, wise but admits her mistakes. Also, she is humorous (when I ask her to be) and very patient.

I decided to see whether ChatGPT can actually do the first stage of screening, i.e. title and abstract screening. After negotiating with her for a few hours, I cracked the code and passed her a carefully worded selection criteria based on PECOS: Population, Exposure, Comparator, Outcome and Study design. And there she was. ChatGPT was telling me whether I should exclude or include a particular study after evaluating a study’s title and abstract.

I used lists of studies and criteria related to this protocol:

Vendl C, Taylor MD, Braeunig J, Gibson MJ, Hesselson D, Neely GG, Lagisz M, Nakagawa S. Profiling research on PFAS in wildlife: Protocol of a systematic evidence map and bibliometric analysis. Ecological Solutions and Evidence. 2021 Oct;2(4):e12106.

What amazed me was that ChatGPT matched the study with our criteria and summarized reasons. Wow, this is better than I can do (see examples: one recommending inclusion and the other recommending exclusion = both are spot on!)

I tested for around 15+ abstracts and ChatGPT was able to reproduce our decisions. So, I stopped there and then started to test whether she can extract some data from the text. This turned out to be more difficult as ChatGPT does not seem to take more than ~2,000 words as an input (although she claims there are no limits). Anyway, as long as I do not give her too much text, ChatGPT seems to be able to extract what animals were studied, PFAS chemicals and locations mentioned, in a format below:

That is all astounding. But some questions remain. How reproducible is this data? Can we make this process much more systematic?

I am hoping to work with a computer scientist and see whether some of these processes can be automated for multiple articles. We are entering an exciting but uncertain time. One thing I can say is that I will be trying to incorporate ChatGPT into some parts of my systematic review workflow from now on, not as a replacement for a human screener but as an addition for now.

DuPont loses challenge over cancer victim's $40 mln verdict in PFAS case

19/12/2022

by Lorenzo Ricolfi

Image by 3D Animation Production Company from Pixabay, modified by adding pfas picture from https://www.setac.org/page/PFAS

On the 5th of December, a federal appeals court of the United States released the verdict on the legal litigation between the American multinational chemical company DuPont and a cancer survivor.

The official verdict document is available here and the full article by Clark Mindock (Reuters) is available here.

Per- and polyfluoroalkyl substances (PFAS) are a group of manufactured chemicals used in various industrial and consumer products. They are also known as "forever chemicals" because they do not break down easily in the environment or the human body. Some PFAS chemicals, including perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), have been linked to specific health effects, including cancer, immune system effects, and developmental effects in infants and children.

In this case, the plaintiff, Travis Abbott, claimed that prolonged exposure to PFOA in his drinking water caused him to develop testicular cancer twice. A jury awarded him $40 million in damages after finding that PFOA was likely the cause of his illness. DuPont, the chemical manufacturer, had argued that Abbott's level of exposure was unlikely to have caused his cancer and had challenged the verdict, claiming it had been unfairly kept from raising defences based on the specifics of Abbott's alleged exposure.

The 6th Circuit Court of Appeals has upheld the jury's verdict, stating that DuPont could not challenge the decision, which relied on a finding in related cases that PFOA was linked to the man's cancer. This case is one of the thousands consolidated in multidistrict litigation (MDL) in Ohio, which claims that DuPont poisoned drinking water by discharging PFOA into waterways from its plant in West Virginia.

Picture from https://btlaw.com/insights/blogs/fast-facts-what-is-pfas

Legal disputes over the adverse health effects of environmental contaminants can be tricky for several reasons. One of the main challenges is the difficulty in proving a causal link between exposure to a particular contaminant and developing a specific health condition. This is because many factors can affect an individual's health, and isolating the effects of a specific environmental pollutant can be challenging. Additionally, the impact of environmental contamination may not manifest for many years, making it difficult to determine the exact cause of a specific health condition. In many cases, the burden of proof is on the plaintiffs to demonstrate that their health problems were caused by exposure to a particular contaminant.

Photo: M.Lagisz

Scientific research plays an essential role in all of this. By studying the chemical and biological properties of specific contaminants, researchers can better understand how these substances interact with living organisms and their surroundings. This information can be used to develop strategies for addressing and mitigating the effects of environmental contamination, such as identifying the source of contamination, developing methods for cleaning up contaminated sites, and implementing policies to prevent future contamination. Additionally, research can help identify the specific health effects of different contaminants and provide guidance on protecting individuals from exposure.

By providing a solid evidentiary base, scientific research can help establish the link between environmental pollution and adverse health effects, which can be crucial in determining the responsibility of polluters and the appropriate remedies to be taken.

Split reference list helper for pilot and collaborative screening rounds

30/11/2022

by Coralie Williams

When screening for a systematic review or meta-analysis, we conduct several pilot screening rounds. Pilot screenings help us refine our search string, decision tree, and increase the overall accuracy of our screening for literature reviews [check out this nice guide from the I-DEEL team for more info: Foo et al, 2021].

During a pilot screening, we want to select a random subset of references that would be a representative sample of the full set. When possible, screening rounds are conducted in collaboration with another reviewer. To speed up the screening process, we sometimes want to randomly allocate a subset of papers to a collaborator by splitting a reference list into subsets.

There are two reasons we’d want to automate the selection and splitting of a reference list:

It is time consuming to randomly select papers (>100 papers is tedious to select by hand!)
We are not really good at selecting things at random (actually computers aren’t really good at selecting truly at random either*)

Below is the R (www.r-project.org) code to run two functions that may come in useful when conducting your pilot and collaborative screenings with Rayyan (https://rayyan.ai/), or any other software where you can upload your pilot reference list.

1. Select random pilot set:

First, load the getpilotref function below in your environment:

# -----------------------------------
# getpilotref function 
# -----------------------------------
## Description: 
#     Function to obtain a random subset of references for pilot screening.
#
# Arguments
# - x: data frame with reference list
# - n: number of papers for pilot subset (default is 10)
# - write: logical argument whether to save the pilot list as a csv file 
#   in the current working directory (default is FALSE).
# - fileName: name of file (default is "pilot")

getpilotref <- function(x, n=10, write=FALSE, fileName="pilot"){
  
  if (length(n) == 1L && n%%1==0 && n>0 && n<=nrow(x)) { 
    
    # sample randomly the vector n of row indexes and remove id column in the final dataset
    x$ids <- 1:nrow(x)
    pdat <- x[which(x$ids %in% sample(x$ids, n)),]
    pilot <- pdat[,-which(colnames(pdat)=="ids")]
    
    } else {
      # error message n value provided is not valid 
      stop("Incompatible value n supplied, please check. 
      #n must be a positive integer no higher than the total number of references provided.") 
    }
  
  if (write==T){
    
    # save generated pilot list in working directory using the name provided
    write_csv(pilot, paste(fileName, ".csv", sep=""), na="")
    
    # print out summary of saved file name
    cat(paste("Pilot random sample set of ", n, " articles is saved as: ", fileName, ".csv", sep=""))
    
  }
  
  return(pilot)
}

Let’s try it out
Load example csv file that was exported from Rayyan (a reference list of papers in Ecology & Evolutionary Biology having the word “butterflies” in their title):


# Read example butterfly reference list
articles<-read.csv("https://raw.githubusercontent.com/coraliewilliams/2022/main/data/articles_butterfly.csv")

First, let’s obtain a random set of 10 papers without saving it as a csv file:

p10 <- getpilotref(articles)

Now, let’s obtain a subset of 100 papers for a pilot screening and save the subset as a csv file called pilot100.csv. Make sure you have the readr package installed and loaded in your environment.

library(readr)
p100 <- getpilotref(articles, n=100, write=T, fileName="pilot100")

## Pilot random sample set of 100 articles is saved as: pilot100.csv

This will save a csv file pilot100.csv in your working directory. If you are unsure where is your working directory run this command getwd() in your console.

2. Split reference list with another collaborator

Load the splitref_prop function in your environment:

# -----------------------------------
# splitref_prop function 
# -----------------------------------
## Description: 
#     Function to split in two a reference list based on input proportions.
#
## Arguments: 
# - x: data frame with reference list
# - p: vector of two numerical proportions for each split, it must have two positive numerical values that sum to 1.
# - write: logical argument whether to save the pilot list as csv in current working directory.
# - fileName: name to give to the suffix of the two split csv files.

splitref_prop <- function(x, p=c(0.5, 0.5), write=F, sname="split") {
  
    if (length(p) == 2L && is.numeric(p) && sum(p) == 1 && all(p > 0)) {
      
      # randomly allocated a numerical id to each reference
      rids <- sample(1:nrow(x))
      
      # get index of row to split on using the proportion values provided
      spl <- floor(p[-length(p)] * nrow(x))
      
      # get indices of two data frames based on split ids
      indx1 <- rids[1:spl]
      indx2 <- rids[(spl + 1):nrow(x)]
      
      # save split subsets in two separate datasets
      split1 <<- x[indx1,]
      split2 <<- x[indx2,]
      
      # print out summary message
      cat(paste(c("Reference list was randomly split into",length(p), "proportions of", p[1]*100, "% and", p[2]*100, "%")))
      
      if (write == T) {
        # save files
        write_csv(split1, paste(sname, "_set1", ".csv", sep = ""), na ="")
        write_csv(split2, paste(sname, "_set2", ".csv", sep = ""), na ="")
        }
      
      } else {
      # error message if provided n value is not valid
      stop("Incompatible values for p (proportions) supplied, please check.
           Proportion values must be positive integers less than 1, and the total sum of all proportions should equal to 1.")
        
    }
}

Let’s try it out
Using the example butterfly reference list, let’s first split the reference list in two equal splits (50% each):

splitref_prop(articles)

## Reference list was randomly split into 2 proportions of 50 % and 50 %

This will give you two separate data frames to share between two reviewers: split1 and split2.

Now let’s get 30% of references in the first subset (split1) and 70% in the second subset (split2), for example if one reviewer has more time to spend on the screening:

splitref_prop(articles, p=c(0.3,0.7))

## Reference list was randomly split into 2 proportions of 30 % and 70 %

Let’s save the 30% and 70% split list of references as csv files with the suffix “testsplit”:

splitref_prop(articles, p=c(0.3,0.7), write=T, sname="testsplit")

## Reference list was randomly split into 2 proportions of 30 % and 70 %

This will save two csv files, testsplit_set1.csv and testsplit_set2.csv, in your working directory.

*computers aren’t really good at selecting truly at random…Random number generators from most computer programs are actually “pseudo-random”, meaning they are produced from a deterministic mathematical model or algorithm. The R code above uses a pseudo-random number generator. Pseudo-random number generators are usually good enough for their intended purpose (basically better than what any human could do). A good pseudo-random number generator will reproduce statistics that are consistent with true randomness, but they are not truly random. A truly random number can be generated based on a constantly changing physical process that can’t be modelled as an algorithm. If you’re curious about true randomness check out these websites: https://www.random.org/; https://qrng.anu.edu.au/random-colours/.

(Any comments, questions or feedback, you can reach me at: coralie.williams@unsw.edu.au)

Say goodbye to fixed- and random-effects meta-analyses

27/10/2022

By Yefeng Yang

As I have been doing more surveys on meta-analytic practices in many disciplines and re-analysing more published meta-analysis (MA) papers, there is one “recommendation” that is growing stronger and stronger in my brain. That is, we should say goodbye to traditional fixed- and random-effects MAs and conduct our MAs using advanced methods like multilevel and multivariate models because meta-analytic datasets are often multilevel and multivariate in nature. Doing so can make sure you properly handle statistical issues like dependency, and heteroscedasticity, resulting in more robust parameter estimations and inferences. My main argument is that in the “worst-case” scenario, where your dataset does not have a complex structure thereof, these advanced models will automatically reduce into a normal fixed- and random-effects models, all with similar (or identical) results to those expected. More importantly, applying advanced methods can help you decompose variances (Figure 1) and separate correlations of true effects from observed effects (Figure 2), delivering new biological insights. I can see the between-study heterogeneity and correlation are overestimated in many published meta-analyses using fixed-and random-effects models.

Figure 1. Imaginary example of hierarchical data structure.

Although these advanced methods are good, there are (at least) three remarks worth noting here. First, all your models should be built strictly based on predefined questions (e.g., a priori hypotheses). Second, before applying these models, you need to correctly understand the statistical theory behind them. Otherwise, you very likely disseminate misleading information if you published results from them. Third (but not the last), do not use complex models to fit a small-sample-size dataset. This is especially true for multivariate models, which are often heavily parameterized (even overparameterized). So, always do (at least some basic) model checking (e.g., likelihood profile, convergence) to ensure stability of your model fitting.

Figure 2. Joint probability distribution (bivariate normal joint density). Photo source: Multivariate normal distribution. (2022, October 16). In Wikipedia. https://en.wikipedia.org/wiki/Multivariate_normal_distribution

As I have been knowing more about statistics, I realised that many methods are just a special form of a more general framework. For example, (two-sample) Student t-test is a special form of ANOVA, which is a special form of linear regression, which is a special form of generalized linear model or linear mixed model, which is a special form of generalized linear mixed model, which is a special form of the generalized additive mixed model. In the same vein, fixed-effect MA is a special form of random-effects MA, which is a special form of a multilevel or multivariate model. I can imagine that one might disagree with “say goodbye to fixed- and random-effects meta-analyses”. For example, fixed-effects MA can still provide valid inferences if limiting your results to the included studies (e.g., conditional inference). I acknowledge this is true as long as you are not goanna generalize results beyond the included studies. I know asking people to resort to complex methods is difficult because people like easily-understandable tools - just think about P-value. I am always open and happy to see different ideas. Lastly, all the above claims only represent my personal intuition and opinion (I might extend it into a paper in future). They might be wrong and do not necessarily speak for my lab’s attitudes toward meta-analyses.

Attending an overseas conference – Ecological Society of America 2022

28/9/2022

by Samantha Burke

After over two years of lockdown, I had the opportunity to leave Australia to attend the Ecological Society of America’s (ESA) joint conference with the Canadian Society for Ecology and Evolution (CSEE). This conference marked my first time presenting an oral talk outside of UNSW. While it was exciting to share my research with others, I found learning about others’ research and networking with new people to be an equally exciting experience.

As my projects consist of systematic-like research, I was thrilled to see ESA created an entire session dedicated to meta-analysis in ecology. Ecologists are relatively new to conducting meta-analysis of their data, so this session was well-attended and directed conversation towards improving meta-science while it’s still in its early stages in ecology. These talks were all excellent and highlighted the upcoming importance and challenges of conducting systematic-like research in ecology and evolutionary biology.

In addition to meeting new people, I was able to connect with researchers I already knew. While in Montreal, I was able to meet I-DEEL’s newest post-doc, April Martinig, in person. April has been working remotely for the past few months, so it was great to attend her presentation on her previous work examining predator-prey interactions in culvert animal passages. As a Canadian citizen, she knew of the best places to go in Montreal, and we chatted over a delicious vegan lunch. We should all look forward to the research she’ll conduct with I-DEEL.

I also had the opportunity to meet members of the Society for Open, Reliable, and Transparent Ecology and Evolutionary biology (SORTEE), of which I’m a member. Even though I went to Canada intending to attend the ESA conference, SORTEE members attending the conference gathered for a mini meetup in Montreal. The society was able to reach out to more ecologists at the conference, and many people came to the meetup to hear firsthand what SORTEE is all about. If you’re interested, please check out a previous blog post by Rose O’Dea and the SORTEE website.

Attending a conference was such a privilege, especially one as diverse as ESA’s 2022 Conference. I look forward to continuing to share my work and learn from others.

SORTEE meetup at the ESA conference. Photo Credit: Dominique Roche

I-deel at ESEB2022 congress

24/8/2022

By Losia Lagisz
13 - 19 August 2022 has been a very busy and fun week – a week at ESEB (European Society for Evolutionary Biology) Congress in Prague, Czech Republic.

This congress was very special to us for five reasons:

We had four I-deel members attending (Shinichi, Losia, Szymek and Patrice) and one associated member (Totoro). Unfortunately, somehow, we do not have a photo with all of us together!
For Shinichi and Losia it was first in-person conference in three years, also first overseas travel since the start of the Covid pandemics. For Totoro it was his first conference ever (and he did very well with his poster presentation).
There were hundreds of great presentations and posters – physically impossible to see them all. The diversity of topics and ideas was exciting and inspiring, as usual at ESEB.
We got to catch up with many of our good old friends and collaborators. We also met many interesting new people.
We organised a SORTEE in-person meet-up, with over 20 people attending from around the world. Some new members potentially will be joining SORTEE and their forces for credibility revolution in ecology and evolution!

Big thanks to the organisers of ESEB2022 and we hope to be able to attend the next one – ESEB2025 to be held in Barcelona, Spain!

Ireland’s Ancient Wild Side: There was once more than just whiskey and stout

17/7/2022

by Kyle Morrison

In today’s world Ireland is famous for vibrant cities, cosy pubs and cold Guinness but, in a simpler time – before us humans got involved, it was once the land of giant deer, grey wolves and grizzly bears. Although, some of these animals can be seen elsewhere, a few sadly cannot and were never seen again. Here I provide you five of the coolest animals that ever roamed the Emerald Isle.

Number 1 – The Great Auk
Despite being coined the original penguin, great auks were not actually penguins at all but a fine product of convergent evolution. Ironically, the Latin name for the great auk is Pinguinus impennis, and when European explorers found the first penguins in the southern hemisphere, they noticed their uncanny resemblance to the great auk and hence we have modern name for penguins. The great auk had a white belly and a black back, stood around 85cm tall and weighed around 5 kgs. It had small wings for swimming and a large beak for eating fish and krill. The great auk was once a common sight along the Irish coastlines with remains being found in popular tourist spots in Donegal and Galway. Much like penguins, the great auk was utterly defenceless on land which unfortunately contributed to their eventual demise in the 1840’s due to widespread hunting for food and bait.

Number 2 – The Irish lynx
Currently, lynxes (or, bobcats) are mainly found across Siberia and North America, but these majestic wildcats were once widespread across the island of Ireland. The presence of Lynx in Ireland wasn’t known until the late 1930’s, when a few hikers found a mandible bone in County Waterford. It’s likely that the Irish lynx roamed the woods and countryside, preying on small deer and hare. Lynxes are known to have survived in the British Isles until the Romans arrived however, there is no indication of when they went extinct in Ireland. Recently, lynx have been considered for a reintroduction project, helping to balance woodland ecosystems and increase biodiversity. The aim is that the introduction of the native lynx will reduce numbers of invasive sika deer, which currently have no natural predator.

Number 3 – The Irish Wolf
Wolves were a major part of the postglacial fauna in Ireland dating back as long ago as 34,000 BC. The Irish word for wolf is Mac Tíre which means “Son of the Countryside”, which illustrates how important wolves were to the people of Ireland. In fact, many Irish stories, myths and folklore are about wolves and how the Irish gods adored them. Before the great agricultural revolution on the island most of the countryside was clothed with thick forest, which was perfect hunting habitat for wolves. It wasn’t until the arrival of Oliver Cromwell in 1650’s that wolves in Ireland became troubled. Cromwell wanted rid of the wolves in Ireland and shockingly ordered a mass culling of all wolves offering £5 for a male, £6 for a female and 40 shillings for a cub. Unfortunately, the number of wolves began to plummet and the last wolf in Ireland was killed in 1786 in County Carlow. Today, there are only a few reminders of the existence of wolves in Ireland through ring forts that were once used to protect sheep, place names and the great Irish Wolfhound.

Number 4 - Irish Bear
For thousands of years brown bears roamed Ireland, preying on deer and fishing in streams for salmon. Much like modern bears in North America, Irish bears hibernated in caves over the long winter months. Amazingly, scientists have revealed DNA evidence that suggests that the Irish bear is the maternal ancestor of Polar bear, which conflicted the previous opinion that North American bears were the ancestors. Additionally, it is thought that the two species may have mated opportunistically during the last 100,000 years which means that they must have interacted during the last ice age. Unfortunately, the Irish brown bear went extinct around 2,500 years ago mostly due to great deforestation and hunting in Ireland. There is a famous Irish myth about a sleeping bear god who will rise from hibernation and come to the aid of their people when called. The summoners of the bear god were called the Mahon’s, the son of the bears. Ironically, the Mahons later became the McMahons which is now a common surname around the world. Today, all that remains to remember the Irish bears are a few sculptures and a Guinness poster.

Number 5 – The Irish Elk
Megaloceros giganteus, the Irish elk, is one of the largest deer that ever lived. It stood at seven feet tall at the shoulder and its antlers spanned an impressive 12 feet wide. Their enormous antlers are thought to be due to sexual selection, a trait to impress females. It had long been thought that their antlers were purely for display but recently scientist have indicated that they may have also been for contests. At their largest males weighed a massive 1,500 lbs, roughly the size of the modern Alaskan moose. Strangely, the Irish elk is not an elk at all but a deer, the name was coined due to its sheer size and the original excavators believed they found the remains of an extinct species of elk. The Irish elk was not exclusive to Ireland but was named so due to their most famous and well-preserved fossils were found in peat bogs across the island. Although impressive, their wide antlers became a maladaptation, and contributed to their eventual extinction in 7,700 BC.

Image sources:

Auk image: https://www.bbc.com/news/science-environment-50563953
Irish Lynx Image: https://www.breakingnews.ie/lifestyle/four-amazing-animals-that-could-be-reintroduced-to-ireland-1019270.html
Ring fort image : https://www.amazing-grace.ie/an-grianan-of-aileach
Irish wolves’ mythology Image: https://earthandstarryheaven.com/2015/05/13/irish-werewolves/
Guinness Bear Image : https://commons.wikimedia.org/wiki/File:Guinness_StoreHouse,_Dublin._Advertising_Exhibit._-_geograph.org.uk_-_626611.jpg
Elk Image: http://news.bbc.co.uk/2/hi/uk_news/northern_ireland/8316262.stm

What a beautiful hypothesis! It explains a lot!

27/6/2022

by Lorenzo Ricolfi

The Italian version of Charles Darwin's The Origin of Species opens with a preface by Luca and Francesco Cavalli-Sforza. They are two of the four children of Luigi Luca Cavalli-Sforza, an Italian geneticist, academic, researcher, and professor emeritus at Stanford University in California, who died in 2018 and became known for his research activities in population genetics. He was also involved in anthropology and history in his studies of human migration.

From https://www.nytimes.com/2018/09/19/obituaries/luigi-cavalli-sforza-dies.html

Since I could not find the English version of the preface anywhere, I would like to translate and summarize it in this article. Therefore, the following text is a summary and translation of the preface written by Luca and Francesco Cavalli-Sforza.

Translation: "It is said that when Laplace, the great French astronomer, presented Napoleon with a copy of his Celestial Mechanics, in which he described universal gravitation and advanced hypotheses on the formation of the solar system, Napoleon remarked: "Mr Laplace, they tell me that you have written this big book on the design of the universe, without ever mentioning its Creator". "This is a hypothesis I did not need", replied Laplace. When Napoleon, amused, reported this conviction to the mathematician Lagrange, he exclaimed: "What a beautiful hypothesis! It explains a lot! ". Two hundred years later, modern texts on astronomy continue to describe the behaviour of celestial bodies without the need for a God creator. In science, no unnecessary hypotheses are introduced to explain events. While no one nowadays argues about divine intervention in the history of the cosmos, a similar question resurfaces from time to time in biology. Since Darwin's time, the theory of evolution has made enormous progress and can explain a great deal of the history of life. Today, our relationship with primates is no longer in question. It has been proven beyond any reasonable doubt.

From Stutz, 2014: https://www.researchgate.net/publication/264417273_Embodied_Niche_Construction_in_the_Hominin_Lineage_Semiotic_Structure_and_Sustained_Attention_in_Human_Embodied_Cognition/citations

Nevertheless, it still meets with the most vigorous resistance from the ultra-conservative fringes of Baptist Christians (a powerful political force in the south of the United States) and ultra-orthodox Jews. On the other hand, it does not seem to create any difficulties for either Catholicism or Islam. What is questioned today is whether evolution is sufficient to explain the extraordinary complexity of life: how is it possible that living beings have developed such a variety of forms? How can an organ such as the eye have achieved its extreme complexity only under natural forces?

From: https://www.phos.co.uk/journal/the-evolution-of-sight

Someone says there must be an Intelligent Design guiding the history of life, intervening in the mechanisms of evolution (with a view to some goal, it is assumed, but this is not stated). The Intelligent Design movement was born as a political fact in the United States; it is promoted by foundations financed by ultra-conservative billionaires and engaged in specific activities, such as supporting those who sue state schools to have the biblical account of creation taught alongside the theory of evolution as an equal alternative. The extreme right-wing label with which the movement was born does not help its spread in Europe, where there has been enough ideology. The absence of scientific arguments makes it fiddly to counter directly. An organism can only live if it interacts with its living environment to obtain food and can only pass on its DNA to the next generation if it becomes an adult and reproduces. However, the environment is constantly changing. Only those who remain 'adapted' to their environment can continue to live. Natural selection acts by automatically filtering, like a rigid sieve, the best types to survive and reproduce, environment by environment and circumstance by circumstance.

From: https://www.britannica.com/science/evolution-scientific-theory/Adaptive-radiation

The theory of evolution by mutation and natural selection says precisely this: living species evolve under the impetus of chance and necessity. Darwin's theory of evolution provides an excellent key to interpreting what we see around us and deepening our knowledge of the molecules that make life possible." - End of translation.

Science and religion have always had harsh disagreements about explaining the existence of the observable universe from the earliest known periods through its subsequent large-scale evolution (of both abiotic and biotic factors). My opinion is that science should not be concerned with the beliefs of others if the views of others do not limit science. But, at the same time, religions should help scientists find the right path following moral rules and ethics. Both science and religion are great powers that give humankind its singularity. Therefore, they should work together to make our species more just, educated and happy.

Farewell and welcome

31/5/2022

Last week, the I-DEEL lab gathered to have a farewell party for Cat who worked on the "PFAS project" for the last 2 years. This project is our lab’s first research synthesis project in environmental sciences, and Cat played a major role. Now she is in Europe and travelling around the world for the next several months (detoxifying PFAS, I presume).

We also welcomed 4 new PhD students to our lab: Lorenzo, Kyle, Coralie and Jess. Lorenzo will further synthesize the PFAS literature while Kyle will work on the pesticide pollution literature. Coralie will develop new meta-analytic tools, working with Prof David Warton. Jess, who did Honours degree with us already, will apply deep learning methods to Australian wildlife image data, working with Prof Richard Kingsford, people from Taronga Zoo, and NSW Wildlife and National Parks.

This is going to be a huge variety of research work - just like the food on the table (see picture above - this is a potluck party where everybody brings a dish!). As they say: “Variety is the spice of life”.
I am very much looking forward to what the future will bring to I-DEEL!