Posted by Olivia Rooke-Devoy (BSc(Hons) Candidate)

If a tree falls in the woods and [we don’t communicate it to someone], does it make a sound?

How important is it that scientists communicate and disseminate their ideas to the wider public?

Credit: Miri Schroeter

Scientific communities now face climate change denial, anti-vaccination movements, ‘detox’ diets and, bizarrely, a resurgence of Flat-Earth believers. In view of these challenges, it seems that science communication is just as important as the science itself. Ultimately, by educating societies, we as researchers encourage better social and political decision making.

However, science communication is challenging. David Chambers’ well-known ‘Draw a Scientist’ test (1983) demonstrates that, from a young age, people view scientists as aloof and antisocial. My own research of urban lawns in Auckland has stirred controversy. Many people have rejected the premise immediately: “I like your idea, but I won’t stop mowing my lawn!”. Preconceived notions of science and the emotive subjects we study make for critical (and often unfriendly) audiences.

Faced with these difficulties, how do we communicate complex scientific ideas, so general audiences understand? Common techniques, such as using less jargon, sound great in theory but are hard in practice. For example, the Ten Hundred Words of Science blog challenges scientists to use the 1000 most commonly used English words to describe their research. Here’s my own attempt:

“What are the impacts of varying mowing regimes on lawn species assemblages in urban lawns?” becomes “what happens if city people cut green low-growing things less?”

Have a go at the challenge yourself.

Explaining scientific ideas in a straightforward way is difficult. However, using ‘simple’ language and crafting a science narrative makes our subjects accessible. Storytelling in science, taking the form of analogies and personal stories of successes and struggles, connects many types of people. This form of communication opens science to previously-excluded groups and makes science more inclusive and diverse.

Contemporary communication is instantaneous and global. In this modern age, what is a ‘scientist’? Overall, I believe that part of what makes a scientist is the ability to communicate ideas. If a tree falls in the woods and you don’t tell me, how can I care that it made a sound?

Further Reading:

Chambers, D. W. (1983). Stereotypic images of the scientist: The draw-a-scientist test. Science Education, 67(2), 255–265. https://doi.org/10.1002/sce.3730670213

Salmon, R., & Priestley, R. (2015). A future for public engagement with science in New Zealand. Journal of the Royal Society of New Zealand, 45(2), 101–107. https://doi.org/10.1080/03036758.2015.1023320

 

Olivia is an Honours student at the School of Biological Sciences, University of Auckland. Her research is focused on encouraging low-cost, biodiverse lawns in Auckland. She is supervised by Dr Bruce Burns. For further information regarding this research, please visit https://urbanlawnsproject.weebly.com/

Posted by Andre M. Bellve (MSc Candidate)

As a quick preface – what I am talking about in this post isn’t new or revolutionary. My intent with this blog is to package some of the facts in a more digestible form and hopefully steer my fellow biologists to better practice.

When we carry out statistical significance tests (I.e. ANOVAs, t-tests, etc) there are two types of errors associated with them: False positives: falsely rejecting the null hypothesis, H₀, and; False negatives: falsely accepting H₀. Every test has these two errors in them and they are inversely proportionate to each other. A significance level of 0.05 means that 5% of the time we will incorrectly reject the null hypothesis. This is the basis of selective reporting, A.K.A p-hacking. Collectively, we have (for the most part) deemed a 5% cut-off as acceptable, at least when we aren’t betting on human lives, and I tend to agree.

What if the chance was higher though? If there was a 10% or 25% chance of false positive, would you still trust your results? You might be thinking: “Well that’s silly Andre – who cuts off their p-values at anything higher than 0.05? ” Well here’s the catch: if you carry out two significance tests, and both have a cut-off for significance at 5%, then the chance that there is a false positive is no longer 5% – it is much higher. This inflating of the overall error rate is the basis of p-hacking. The chance of a false positive is also increased when making pairwise/multiple comparisons!

Credit: Statistical Statistic Memes via Facebook

Now this isn’t the end of the world – you can correct for it. The read significance level that you set as your cut-off defines the overall error rate of your experiment – both the type I and II error rates. If you are doing several tests, then you have to correct for the increased chance of a false positive. Fortunately, it’s relatively easy to do! There is the extremely conservative Bonferroni’s correction where you divide your significance level by the number of tests you are doing, which is appropriate in some cases (i.e. when it’s a case of life or death). This isn’t popular among biologists as we often deal with very noisy data and it can be hard enough to pick up a signal as it is. For this reason, the less conservative False Discovery Rate (FDR) correction works well when lives aren’t on the line, as it typically leaves at least one significant result without sacrificing the integrity of your analysis. This is not an exhaustive list and you can read more about different methods for correction here.

Selective reporting happens a lot. One recent example came out of the sensory science food labs from Cornell University: Brian Wansink, director of the Food and Brand lab at Cornell, boasted online that a volunteer research assistant of his was able to take a “failed study which had null results” and produce ‘significant’ results from it (Science of Us, 2016). However, careful investigation of the published papers yielded multiple errors and inconsistencies suggesting that the research assistant had ‘p-hacked’ the data to produce these results, although it seemed she had done so unwittingly. It is this kind of behaviour that brought about an analysis by Ioannidis (2005) which found that most published research findings are actually false. The incorrect use of these methods can put at risk the objectivity of the traditional scientific method and create issues of credibility for the scientific community. With more “fake news” and anti-science rhetoric being thrown around than ever before the last thing we need are blows to our credibility or any more fuel being added to the tangerine tire fire that is my president.

Image Credit: politicususa.com

References

A Big Diet-Science Lab Has Been Publishing Shoddy Research — Science of Us. (n.d.). Retrieved April 8, 2017, from http://nymag.com/scienceofus/2017/02/cornells-food-and-brand-lab-has-a-major-problem.html

Ioannidis, J. P. (2005). Why most published research findings are false. PLos Med, 2(8), e124.

 

 

Posted by Tom Bodey

Scientists are always trying to communicate their research and ideas across a wide spectrum of media to varying degrees of success (and I can already feel the hoisting of my own petard here). Some of the difficulties arise because a researcher can apply caution and caveating to their results, whereas recipients may prefer to see a clear-cut outcome that makes for more straightforward decision-taking for example. However, scientists do not always help themselves, either by using jargon, or through the avoidance of terms because of the connotations they may imply.

For my latest research I have decided to enter one of these minefields by looking at individual variation in behavioural responses – you see, I’ve done it myself. This variation, particularly if examined across contexts, has been given a range of names within the scientific literature – behavioural syndromes, coping styles, behavioural tendencies – all of which shy away from the dreaded p word – ‘personality’. Now, of course, there are obvious reasons why a researcher may choose to avoid this word, and certainly to use it within air quotes. After all, without becoming Dr Doolittle, it’s pretty hard to get into a non-human head to actually assess their personality, and what you have instead is the examination of a handful of behaviours where individuals differ in their responses.

However, even on these highly simplified ‘personality’ scales, it is becoming increasingly obvious that individual animals often differ substantially, and also consistently, in their behaviour. For example, some individuals from species as different as prawns, hyenas and albatross can be bolder, more active, or more social than others, and this variation can then affect other aspects of their lives such as how they find food. These differences are especially intriguing as the maintenance of variation stands in contrast to the movement of species towards a fitness peak. In order to maintain variation, there must be situations in which one set of behaviours are beneficial, and contrasting situations where different individuals prosper – any peak must shift through time or space.

Not content with dipping my toe into an area with such linguistic juggling, I thought I would combine this with invasive species – another area where terminology can take on unfortunately loaded connotations – by looking at individual variation across a range of niche dimensions in everyone’s favourite critter, the humble rat. The Chinese, of course, recognise the rat as the first animal within their zodiac cycle, embodying alertness, spirit and intelligence (as well as being timid, devious and gossipy to balance it out). New Zealand is ‘blessed’ with three species – all invasive and globally widespread – the Brown/Norway Rat Rattus norvegicus, Black/Ship Rat R rattus and the Pacific Rat/Kiore R exulans. I’ve been catching wild rats on offshore islands in the beautiful Hauraki Gulf, running them through behavioural trials in the field before releasing them in order to do it all again the next day, and the next.

Right now I just have a lot of home video, but ultimately this work should provide insights into ecological theory – for example, the ways in which interference competition affects niche space occupancy at individual and species levels. It should also have applied applications for invasive species management. New Zealand, like many oceanic islands, is in the position of having no native terrestrial mammals, and thus can manage invasive mammals, should they choose to do so, in relatively straightforward and robust ways. However, elsewhere management must avoid harming native mammal species. Triaging of the system, making adjustments to maximise the chances of catching the individuals with the greatest potential to cause harm, would be the most cost-effective way forward if eradication is not possible or desirable. And if eradication is the goal, then you need to know there aren’t individuals that will avoid all control mechanisms. Either way, identifying those individuals with a ‘troublemaking personality’ (as any press release might say but any scientific paper would not) is key.

Posted by Simon Connolly

Invertebrates are (ironically) the backbone of every ecosystem in New Zealand, providing multiple essential services. It is troubling to note then that in NZ over 1000 invertebrate species are classified as Threatened or At Risk (full lists can be found here).  However, despite evidence they can benefit from conservation action, they are often ignored in conservation policy.

There are exceptions to this rule. Notably, the Cromwell Chafer Beetle was saved from extinction by the world’s first nature reserve dedicated to the protection of an invertebrate species (read more about this here).

The simplest reason for the lack of invertebrates in conservation work is the fact that they are very difficult to study, because of their staggering species diversity. The bane of entomologists is the ‘taxonomic impediment’, meaning that there are so many invertebrate species that we do not have names for all of them yet. I’m sure you can imagine the impossibility of trying to come up with recovery plans for species that aren’t even known to science. This diversity also increases the need for specialist work in terms of distinguishing threatened species from common close relatives.

To classify a species as threatened requires a lot of knowledge about its populations, range, and other related factors. Such data are not as easy to gather as they are for vertebrate species, given that invertebrates are often hard to see, making it difficult to come to any conclusions at all. It is no coincidence that the Weta, easily the most iconic group in NZ invertebrate conservation, are also the largest and most noticeable.

A more uncomfortable truth is that we as human-beings like to play favourites. Charismatic and cute vertebrates tug at our heartstrings, and, for most, invertebrates will never hold the same lustre despite their importance to the world. Inevitably, the species we care about more receive more conservation resources.

What can be done? Well, research in the scientific space is looking at whether we can more effectively use the data we have. This includes using known locations of invertebrates to build up a picture of their climatic preferences or looking at a species’ physical traits to speculate on the threats it may face in the wild. Hopefully, you’ll hear more about this at the end of my Master’s project (fingers crossed). Is this a magic bullet? Certainly not, but hopefully it’s a step in the right direction.

For now, I hope when you think of threatened species you will remember that there are hundreds of species that need our attention and some of them are much less cute, but no less important than others.

Simon is a Masters Student at the School of Biological Sciences, University of Auckland. His research is focused on threatened insects and he is supervised by Darren Ward.