Showing posts with label science. Show all posts
Showing posts with label science. Show all posts

Thursday, March 8, 2018

The Gender-Biased Scientist: Women in Science

Guest post by Maika Seki, MEnvSci Candidate in the Professional Masters of Environmental Science program at the University of Toronto-Scarborough

In November of 2017, Nature Ecology & Evolution published “100 articles every ecologist should read” by Courchamp and Bradshaw, sparking a social media outrage. Rightfully so, because the list of first authors only included two women. There remains a pervasive perception that women lack the skills to practice science, and that there simply are not enough women in the field for them to have made a significant contribution, referring to the male-dominated history of the sciences. Many of us have come across studies highlighting gender bias in science education - which people have attempted to use to explain gender gaps in STEM fields. However in 2011, neuroscientist Melissa Hines found no significant difference between the mathematical, spatial, and verbal skills of boys and girls. But of course that finding did not receive much attention. In light of the emerging discourse of vital inclusivity in science, now is the time to confront our own social biases with the goal of achieving gender equity in the scientific community.

Instead of rehashing these outdated arguments, why don’t we talk about the barriers that women face in science? Why don’t we talk about the sexism in the publishing and peer-review process? In 2015, evolutionary geneticist Fiona Ingleby submitted a research paper to PLOS ONE, where the peer-reviewer suggested that she work with male biologists in order to strengthen the study, stating, “It would probably … be beneficial to find one or two male biologists to work with (or at least obtain internal peer review from, but better yet as active co-authors).” The under-recognition of women scientists has been so rampant in the fabric of science that it has been coined the Matilda effect; named after the first women scientist to comment on the phenomenon, Matilda Jocelyn Gage.
   
Why don’t we talk about the barriers women face in accessing employment in science, even while possessing the same qualifications as their male counterparts? At Yale University, a study was conducted wherein over 100 scientists assessed a resume for a job posting. The only difference between the resumes were the names; half of them were given recognizably male names, and the other half recognizably female names. The resumes submitted under the female names were deemed significantly less competent and employable, and were offered lower salaries. Clearly there is work to be done.

And then there was Tim Hunt, a Nobel laureate who made outright sexist comments at the World Conference of Science Journalists stating, “Let me tell you about my trouble with girls … three things happen when they are in the lab … You fall in love with them, they fall in love with you and when you criticize them, they cry.Twitter responded with the hashtag #DistractinglySexy, where women scientists shared unglamorous photos of them doing their research work. Hunt subsequently resigned from his honorary post at the University College-London. We may think that this is an exceptional and isolated event, but studies show that we are not immune to these kinds of social forces of gender discrimination, even if we like to think so — especially as scientists. These seemingly minor micro-aggressions translate to devastating and tangible effects, such as the gender pay gap. 





Photo by @STEPHEVZ43 on Twitter, as a response to Tim Hunt’s sexist comments.



Within scientific fields, we like to pride ourselves in being as close to bias-free as possible with our empirical, quantitative, and reproducible data. But scientists are people, and as such, we must confront the cultural and social influences that may permeate our objectivity. As scientists, we do not like to admit to this. But if we are going to arrive as close to the truth as possible, we need to capitalize on the emerging discourse of gender issues in science.
    
As of 2015, Canadian women represented only 22% of the STEM workforce. Not only are women under-represented in the workforce despite 62% of undergraduate students being women, but they are under-compensated. According to Statistics Canada, the wage gap persists across all fields, with the women median income of a bachelor’s degree being $68,342, and $82,083 for men. This is not a “third world” problem. This is a global issue. It is indisputable that there are systemic barriers that women face when pursuing careers in science. So why can’t scientists consider the confounding social factors at play that create these patterns? In science when somebody denies a phenomenon after many analyses point to the same mechanism, we would likely consider that as being irrational. With this in mind, is the denial of gender bias in science not irrational? By acknowledging these biases and promoting change, we take aim at the lack of objectivity in the discipline of science. It should also be encouraged to confront the sexism, racism, and all other intersectionalities of power imbalance within the science community. Some may argue that there is no place for politics in science, but we must face the reality that the two can not be separated. Addressing the sexism would bring us better, more balanced science. 


Statistics Canada graph on the Canadian men and women in STEM fields.


How can we aspire towards a world of innovation and ground-breaking research when roughly half of the population is held back? And how can we address it? To start, we need to hold institutions more accountable. It is disheartening to know that had people not reacted to the all-male panels, it would not be seen as a problem. Furthermore, it is not enough to tweet about it. It’s a start, but not nearly enough — because how many of these types of stories repeat themselves in the media? We need it to be written in the mandates of institutions, and this is not enough. We need it to be enforced. We also need women to be more involved and hold power in these decision-making panels; it is not enough to throw in a token white woman and call it a day. It is not enough for women to be given a seat on the board as a corporate marketing tool under the guise of inclusivity. They must also be afforded the same power that men have. We need to hold each other more accountable. We need to confront our own prejudices, no matter how uncomfortable that may be. If not for women, then do it for practical and selfish reasons; do it because there are studies that show that women have to be more productive than men to be deemed equally scientifically competent (feeling the pressure to prove themselves). And do it because it is better for the economy, and because diversity in the workplace increases productivity




Graph by The Star on the income of full-time men and women in Canada, who have a bachelor’s degree.


There is no good reason to continue to exclude women from the same influential roles that men have, and it is time that we each consider our own sexist views (whether sub-conscious or not). It is time to challenge the systemic biases in powerful institutions in order to let women claim their full potential as true peers to men; as colleagues, partners, scientists, and in all other walks of life. In order to increase scientific literacy, we can not afford to continue to exclude women from science, because science needs women. In the spirit of the United Nations’ International Day of Women and Girls in Science day, which passed on February 11th, and International Women’s day today, let us commit to empowering women to reach political, social, and economic equality to men. And let us make changes in our own lives, begin conversations with those around us, and become more active in our communities to progress towards gender equity.


Friday, March 4, 2016

Pulling a fast one: getting unscientific nonsense into scientific journals. (or, how PLOS ONE f*#ked up)

The basis of all of science is that we can explain the natural world through observation and experiments. Unanswered questions and unsolved riddles are what drive scientists, and with every observation and hypothesis test, we are that much closer to understanding the universe. However, looking to supernatural causes for Earthly patterns is not science and has no place in scientific inquiry. If we relegate knowledge to divine intervention, then we fundamentally lose the ability to explain phenomena and provide solutions to real world problems.

Publishing in science is about leaping over numerous hurdles. You must satisfy the demands of reviewers and Editors, who usually require that methodologies and inferences satisfy strict and ever evolving criteria -science should be advancing. But sometimes people are able to 'game the system' and get junk science into scientific journals. Usually, this happens by improper use of the peer review systems or inventing data, but papers do not normally get into journals while concluding that simple patterns conform to divine intervention.

Such is the case in a recent paper published in the journal PLOS ONE. This is a fairly pedestrian paper about human hand anatomy and they conclude that anatomical structures provide evidence of a Creator. They conclude that since other primates show a slight difference in tendon connections, a Creator must be responsible for the human hand (well at least the slight, minor modification from earlier shared ancestors). Obviously this lazy science and an embarrassment to anyone that works as an honest scientist. But more importantly, it calls into question the Editor who handled this paper (Renzhi Han, Ohio State University Medical Center), but also PLOS ONE's publishing model. PLOS ONE handles thousands of papers and requires authors to pay for the costs of publishing. This may just be an aberration, a freak one-off, but the implications of this seismic f$@k up, should cause the Editors of PLOS to re-evaluate their publishing model.  

Wednesday, August 26, 2015

Science is a maze

If you want to truly understand how scientific progress works, I suggest fitting mathematical models to dynamical data (i.e. population or community time series) for a few days.
map for science?

You were probably told sometime early on about the map for science: the scientific method. It was probably displayed for your high school class as a tidy flowchart showing how a hypothetico-deductive approach allows scientists to solve problems. Scientists make observations about the natural world, gather data, and come up with a possible explanation or hypothesis. They then deduce the predictions that follow, and design experiments to test those predictions. If you falsify the predictions you then circle back and refine, alter, or eventually reject the hypothesis. Scientific progress arises from this process. Sure, you might adjust your hypothesis a few times, but progress is direct and straightforward. Scientists aren’t shown getting lost.

Then, once you actively do research, you realize that formulation-reformulation process dominates. But because for most applications the formulation-reformulation process is slow – that is, each component takes time (e.g. weeks or months to redo experiments and analyses and work through reviews) – you only go through that loop a few times. So you usually still feel like you are making progress and moving forward.

But if you want to remind yourself just how twisting and meandering science actually is, spend some time fitting dynamic models. Thanks to Ben Bolker’s indispensible book, this also comes with a map, which shows how closely the process of model fitting mirrors the scientific method. The modeller has some question they wish to address, and experimental or observational data they hope to use to answer it. By fitting or selecting the best model for they data, they can obtain estimates for different parameters and so hopefully test predictions from they hypothesis. Or so one naively imagines.
From Bolker's Ecological Models and Data in R,
a map for model selection. 
The reality, however, is much more byzantine. Captured well in Vellend (2010)
“Consider the number of different models that can be constructed from the simple Lotka-Volterra formulation of interactions between two species, layering on realistic complexities one by one. First, there are at least three qualitatively distinct kinds of interaction (competition, predation, mutualism). For each of these we can have either an implicit accounting of basal resources (as in the Lotka-Volterra model) or we can add an explicit accounting in one particular way. That gives six different models so far. We can then add spatial heterogeneity or not (x2), temporal heterogeneity or not (x2), stochasticity or not (x2), immigration or not (x2), at least three kinds of functional relationship between species (e.g., predator functional responses, x3), age/size structure or not (x2), a third species or not (x2), and three ways the new species interacts with one of the existing species (x3 for the models with a third species). Having barely scratched the surface of potentially important factors, we have 2304 different models. Many of them would likely yield the same predictions, but after consolidation I suspect there still might be hundreds that differ in ecologically important ways.”
Model fitting/selection, can actually be (speaking for myself, at least) repetitive and frustrating and filled with wrong turns and dead ends. And because you can make so many loops between formulation and reformulation, and the time penalty is relatively low, you experience just how many possible paths forward there to be explored. It’s easy to get lost and forget which models you’ve already looked at, and keeping detailed notes/logs/version control is fundamental. And since time and money aren’t (as) limiting, it is hard to know/decide when to stop - no model is perfect. When it’s possible to so fully explore the path from question to data, you get to suffer through realizing just how complicated and uncertain that path actually is. 
What model fitting feels like?

Bolker hints at this (but without the angst):
“modeling is an iterative process. You may have answered your questions with a single pass through steps 1–5, but it is far more likely that estimating parameters and confidence limits will force you to redefine your models (changing their form or complexity or the ecological covariates they take into account) or even to redefine your original ecological questions.”
I bet there are other processes that have similar aspects of endless, frustrating ability to consider every possible connection between question and data (building a phylogenetic tree, designing a simulation?). And I think that is what science is like on a large temporal and spatial scale too. For any question or hypothesis, there are multiple labs contributing bits and pieces and manipulating slightly different combinations of variables, and pushing and pulling the direction of science back and forth, trying to find a path forward.

(As you may have guessed, I spent far too much time this summer fitting models…)

Wednesday, July 8, 2015

Taking stock of exotic species in the new wild: Acknowledging the good and the bad.*

Are exotics good or bad? They are neither. They just are. But some exotics cause harm and impede certain priorities, and debates about exotics often ignore reality.

Book review: Fred Pearce. 2015. The New Wild: Why Invasive Species Will Be Nature’s Salvation. Beacon Press

There has been much soul-searching in invasion biology, with attacks, and subsequent rebuttals, on the very nature of why we study, manage and attempt prevent the spread of exotic species (Davis et al. 2011) (Alyokhin 2011, Lockwood et al. 2011, Simberloff 2011). What is needed at this juncture is a thoughtful and balanced perspective on the nature of the discipline of biological invasion. Unfortunately, the book “The New Wild” authored by Fred Pearce, is not that balanced treatment. What is presented in this book is a very one-sided view, where counter-evidence to the thesis that exotics will save nature is most often overlooked, straw men are erected to aid in this goal, and the positions of working ecologists and conservation biologists are represented as simplistic, anachronistic or just plain incorrect.

What Pearce has written is a book-long argument about why exotics shouldn’t be feared, but rather embraced as a partial solution to anthropogenic land use change. I do not wish to undermine the reality that exotics can play important roles in urban landscapes, or that some ecologists and conservation biologists do indeed harbour suspicions of exotics and subscribe to unrealistic notions of purely native landscapes. Exotic policy is at the confluence of culture, science, economics and politics, and this is why the science is so valuable (Sandiford et al. 2014). For Pearce, the truth of what most ecologists do and think seems like an inconvenient reality.  There are a number of pervasive, frustrating problems with Pearce’s book, where bad arguments, logical flaws and empirical slight-of-hand obfuscate issues that desperately need honest and reflective treatment.

A monoculture of the exotic plant Vincetoxicum rossicum that spans open and understory habitats near Toronto, Canada (photo by M. Cadotte). This is a species that interferes with other management goals and needs to be actively managed.


There are major problems with ‘The New Wild’ and these include:

1) A premise built on a non sequitur and wishful thinking. The general premise of the book, that exotics represent a way out of our environmental doldrums, is myopic. Pearce’s reasoning seems to be that he has conflated “the world is not pristine and restoration is difficult…” with the alternative being that exotics are positive and “we should bring them on”. Certainly we can question exotic control efficacy, costs and conservation goals, but that does not mean that exotics are necessarily the solution.

      2)   Underrepresenting the observed effects of some invasive non-indigenous species. Pearce’s book is not balanced. The perceived benefits of exotics in this ‘New Wild’ are extolled while dismissing some of the problems that invasive ones might cause. He says that exotics typically “die out or settle down and become model eco-citizens” (p. xii). But there is a third outcome that Pearce ignores –they move in and become unruly neighbours. When he must acknowledge extinctions, he minimizes their importance. For example when discussing Hawaiian bird extinctions: “The are only 71 known extinctions” (p. 12 –italics mine), or with California: “But only 30 native species are known to have become extinct as a result [of exotics]” (p. 64 –italics mine).

He also implies throughout the book that exotics increase diversity because “Aliens may find new jobs to do or share jobs with natives.” (p. 113). The available evidence strongly suggests that the numbers of species inhabiting communities has not increased over time (Vellend et al. 2013, Dornelas et al. 2014). Which on the surface seems like a good thing, except that many communities are now comprised of 20-35% exotics. This means that there have been losers. Vellend and colleagues (2013) show that the largest impact on native species diversity has been the presence of exotics. So, they do not necessarily find new jobs, but rather outcompete some natives.

      3)   Conservation biologists and ecologists in the crosshairs. Pearce continually lauds those like-minded, outspoken advocates of exotics while belittling ecologists and conservation biologists who don’t agree with him. His disrespect for the process of science comes in two forms. First, he seldom considers evidence or presents opinions counter to his thesis. He gives a partial reason about this bias; he says that ecologists (except for those few brave pioneering souls) ignore novel ecosystems and the functional contributions of exotics (for example on p. 13). This is demonstrably false (see next section). Pearce has little affection for conservation biologists and mainstream ecologists. Both groups are disparaged and dismissed throughout the book. Conservation biologists get a particularly rough ride, and he never acknowledges the difficulty of their task of balancing multiple priorities: extinction vs. ecosystem function, habitat preservation vs. socioeconomic wellbeing, etc. For example, Pearce states: “Conservation scientists are mostly blind to nature outside of what they think of as pristine habitats and routinely ignore its value” –again a demonstrably false assertion.

In a particularly galling example, Pearce resorts to ‘guilt by association’ as an ad hominem attack to undermine the validity of opposing views. He links conservation with eugenics: “Many conservationists of the first half of the twentieth century were prominent proponents of eugenics” (p. 141). It would be equally valid to state that most journalists were proponents of eugenics in the first half of the twentieth century. Pearce, being a journalist, should see this as a specious argument at best.

Ecologists share in this odd and unfair derision. “Ecologists are tying themselves in knots because they refuse to recognize that these novel, hybrid ecosystems are desirable habitats for anything.” (p. 156). Unfortunately for Pearce, there are more than 4000 papers on ‘novel ecosystems’.

      4)   Misrepresenting modern ecology and conservation. Pearce attacks ecological science throughout the book and as an example Pearce makes observations about the role of disturbance and refusal to acknowledge this by ecologists “intent on preserving their own vision of balanced nature” (p. 144). However, disturbance has been a central component of community ecology for the past five decades. Because of this balance-of-nature view, Pearce says ecologists are not studying degraded, disturbed or recovering systems. For example, with secondary forests, he says: “Yet the blinkered thinking persists. Degraded forests and forests in recovery are almost everywhere under-researched and undervalued.” (p. 157). Yet there are almost 9,500 papers on secondary forests –highlighting the ecological interest in these widespread systems. There are numerous such examples.

      5)   A black and white, either-or dichotomy.  What Pearce provides is a series of stark dichotomies with little room for subtle distinction. He ties resilience and ecosystem wellbeing to the arrival of exotics, without adequately assessing the drawbacks: “Nature’s resilience is increasingly expressed in the strength and colonizing abilities of alien species …we need to stand back and applaud” (p. xii).

Invariably in ecology, debates over ‘either/or’ dichotomies end up with the realization that these dichotomies are endpoints of a continuum. This is exactly the case with exotics. Are they bad or good? The answer is neither. They just are. Some exotics species provide economic opportunity, ecosystem services and help meet other management goals. Some exotics cause harm and impede certain priorities. Modern management needs to be, and in many cases is, cognizant of these realities.

 References
Alyokhin, A. 2011. Non-natives: put biodiversity at risk. Nature 475:36-36.
Davis, M. A., M. K. Chew, R. J. Hobbs, A. E. Lugo, J. J. Ewel, G. J. Vermeij, J. H. Brown, M. L. Rosenzweig, M. R. Gardener, and S. P. Carroll. 2011. Don't judge species on their origins. Nature 474:153-154.
Dornelas, M., N. J. Gotelli, B. McGill, H. Shimadzu, F. Moyes, C. Sievers, and A. E. Magurran. 2014. Assemblage Time Series Reveal Biodiversity Change but Not Systematic Loss. Science 344:296-299.
Lockwood, J. L., M. F. Hoopes, and M. P. Marchetti. 2011. Non-natives: plusses of invasion ecology. Nature 475:36-36.
Sandiford, G., R. P. Keller, and M. Cadotte. 2014. Final Thoughts: Nature and Human Nature. Invasive Species in a Globalized World: Ecological, Social, and Legal Perspectives on Policy:381.
Simberloff, D. 2011. Non-natives: 141 scientists object. Nature 475:36-36.
Vellend, M., L. Baeten, I. H. Myers-Smith, S. C. Elmendorf, R. Beauséjour, C. D. Brown, P. De Frenne, K. Verheyen, and S. Wipf. 2013. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. Proceedings of the National Academy of Sciences 110:19456-19459.

 *This post is a synopsis of my book review in press at Biological Invasions