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Instead of rehashing these outdated arguments, why don’t we talk about the barriers that women face in science?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.

-Source: http://www.bbc.com/news/blogs-trending-33099289

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.

-Source: http://www.macleans.ca/society/science/gender-inequality-in-the-sciences-its-still-very-present-in-canada/

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.

-Source: https://www.thestar.com/news/canada/2017/11/29/gender-pay-gap-persists-census-data-shows.html

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 11^th, 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.

Moving on up to the regional scale

Like the blind men and the elephant, perspective drives understanding in ecology. The temporal and spatial scale of analysis (let alone the system and species you focus on) has major implications for your conclusions. Most ecologists recognize this fact, but consider only particular systems, scales or contexts due to practical limitations (funding, reasonable experimental time frames, studentship lengths). 

Ecologists have long known that regional processes affect local communities and that local processes affect regional patterns. Entire subfields like landscape ecology, metapopulations, metacommunities, and biogeography (species area relationships) highlight these spatial dependencies. But high-profile ecological research into biodiversity and ecosystem functioning ('BEF') primarily considers only local communities. Recently though, the literature has started to fill this gap and asking what BEF relationships look like at larger spatial scales, and how well local BEF relationships predict those at larger spatial scales.

'Traditional' BEF experiments were done at relatively small spatial scales (often only a few meters^2). Positive BEF relationships were commonly observed, but often were quite saturating – that is, only a few species were necessary to optimize the function of interest. If the impact of biodiversity saturates with only a few species, it would seem that surprisingly few species are necessary to maintain functioning. True, studies that considered multiple ecosystem functions are more likely to conclude that additional diversity is required for optimal functioning (e.g. Zavaleta et al. 2010). But a simplistic evaluation of the facts that a) ecosystem functioning rapidly saturates with diversity, and b) locally, diversity may not be generally decreasing (Vellend et al. 2017), could lead to overly confident conclusions about the dangers of biodiversity loss. Research on BEF relationships, as they transition from local to larger spatial scales, is increasingly suggesting that our understanding is incomplete, and that BEF relationships can grow stronger at large spatial scales.

A number of recent papers have explored this question, and in considering the essential role of spatial scale. Predictions about how BEF relationships will change with spatial scale vary. On one hand, in most systems there are only a few dominant species and these species may disproportionately contribute to ecosystem functions, regardless of the spatial scale. On the other hand, species-area relationships tend to increase rapidly at small scales, as community composition turns over. If that is the case, then different species may make important contributions in different places. Winifree et al. (2018) contrasted these predictions for three crop species that rely on natural bee pollinators (cranberries, blueberries, and watermelons). They censused pollinators at 48 sites, over a total extent of ~3700 km^2. Though at local scales very few bee species were required to reach pollination goals, the same goals at larger spatial scales required nearly an order of magnitude more bee species. These results in particular appeared to be driven by species turnover among sites--perhaps due to underlying environmental heterogeneity.

From Winifree et al. "Cumulative number of bee species required to maintain thresholds of 25% (orange), 50% (black), and 75% (purple) of the mean observed level of pollination, at each of n sites (16). Horizontal dashed lines indicate the total number of bee species observed in each study. Error bars represent 1 SD over all possible starting sites for expanding the spatial extent. For all three crops combined, each x-axis increment represents the addition of one site per crop".

Another mechanism for increased BEF at larger scales is insurance effects. The presence of greater diversity can interact with spatial and temporal environmental variation to increase or stabilize ecosystem functioning. Greater diversity should maximize the differential responses of species to changing conditions, and so buffer variation in ecosystem functioning. Such effects, when they occur through time are temporal insurance, and when they occur via dispersal among sites, spatial insurance. Wilcox et al. (2018) considered the role of synchrony and asynchrony among populations, communities, and metacommunities to ask whether local asynchrony affected stability (see Figure below for a nice conceptual explanation). Across hundreds of plant data sets, they found that asynchrony of populations did enhance stability. However, the degree to which it affected stability varied from very weak to very important (e.g. by 1% to 300%). Maximizing species or population differences at local scales apparently can have implications for dynamics, and so potentially stability of functioning, at much larger scales.

From Wilcox et al. "Conceptual figure showing how stability and synchrony at various spatial scales within a metacommunity combine to determine the stability of ecosystem function (here, productivity). In (a), high synchrony of species within and among local communities results in low stability at the scale of the metacommunity. In (b), species remain synchronised within local communities, but the two communities exhibit asynchronous dynamics due to low population synchrony among local patches. This results in relatively high gamma stability. Lastly, in (c), species exhibit asynchronous dynamics within local communities through time, and species-level dynamics are similar across communities (i.e. high population synchrony). This results in relatively high gamma stability. Blue boxes on the right outline stability components and mechanisms, and the hierarchical level at which they operate. Adapted from Mellin et al. (2014)."

Finally, Isbell et al. (2018) describe ways in which ecosystem functioning and other contributions of nature to humanity are scale-dependent, laying out the most useful paths for future work (see figure below).

From Isbell et al. 2018.

These papers make nearly identical points worth reiterating here: 1) we have done far too little work beyond the smallest spatial scales (~3 m^2) and so lack necessary knowledge of the impacts of losing of biodiversity, and 2) policy decisions and conservation activities are occurring at much larger scales – at the scale of the park, the state, or the nation. Bridging this gap is essential if we are to make any reasonable arguments as to why ecosystem function figure into  large-scale conservation activities.

References:
Sustaining multiple ecosystem functions in grassland communities requires higher biodiversity. Erika S. Zavaleta, Jae R. Pasari, Kristin B. Hulvey, G. David Tilman. Proceedings of the National Academy of Sciences Jan 2010, 107 (4) 1443-1446; DOI: 10.1073/pnas.0906829107. 

Plant biodiversity change across scales during the Anthropocene. Vellend, Mark, et al. Annual review of plant biology 68 (2017): 563-586.

Species turnover promotes the importance of bee diversity for crop pollination at regional scales. RACHAEL WINFREE, JAMES R. REILLY, IGNASI BARTOMEUS, DANIEL P. CARIVEAU, NEAL M. WILLIAMS, JASON GIBBS. SCIENCE16 FEB 2018 : 791-793

Asynchrony among local communities stabilises ecosystem function of metacommunities. Kevin R. Wilcox, et al. Ecology Letters. Volume 20, Issue 12, Pages 1534–1545.

Isbell, Forest, et al. "Linking the influence and dependence of people on biodiversity across scales." Nature 546.7656 (2017): 65.

A general expectation for the paradox of coexistence

There are several popular approaches to the goal of finding generalities in ecology. One is essentially top down, searching for generalities across ecological patterns in multiple places and at multiple scales and then attempting to understand the underlying mechanisms (e.g. metabolic scaling theory and allometric approaches). Alternatively, the approach can be bottom up. It may consider multiple models or multiple individual mechanisms and find generalities in the patterns or relationships they predict. 

A great example of generalities from multiple models is in a recent paper published in PNAS (from Sakavara et al. 2018). It relies on, links together, and adds to, our understanding of community assembly and the effects of competition on the distribution of niches in communities. In particular, it adds additional support to the assertion that both combinations of either highly similar or highly divergent species can coexist, across a wide variety of models.

Work published in 2006 by Scheffer and van Nes played an important early role towards a reconciliation of neutral theory and niche-based approaches. They used a Lotka-Volterra model to highlight that communities could assemble with clusters of coexisting, similar species evenly spaced along a niche axis (Figure 1). Neutrality, or at least near-neutrality, could result even when dynamics were determined by niche differences. [Scheffer, van Nes, and Remi Vergnon also provide a nice commentary on the Sakavara et al. paper found here].

Fig. 1: From Scheffer and van Nes, emergent 'lumpiness' in communities.

One possibility is that Scheffer and van Nes's results might be due to the specifics of the L-V model rather than representing a general and biologically realistic expectation. Sakavara et al. address this issue using a mechanistic consumer-resource model in "Lumpy species coexistence arises robustly in fluctuating resource environments". Under this model, originally from Tilman's classic work with algae, coexistence is limited by the number of resources that limit a species' growth. For 2 species, for example, 2 resources must be present that limit species growth, and further the species must experience a tradeoff in their competitive abilities for the 2 resources. Coexistence can occur when each species is limited more by the resource on which it is most competitive (Figure 2). Such a model– in which resources limit coexistence—leads to an expectation that communities will assemble to maximize the dissimilarity of species.

Fig 2. From Sakavara et al. (2017).

Such a result occurs when resources are provided constantly, but in reality the rates of resource supply may well be cyclical or unpredictable. Will community assembly be similar (resulting in patterns of limiting similarity) when resources are variable in their supply? Or will clumps of similar species be able to coexist? Sakavara et al. considered this question using consumer-resource models of competition, where there are two fluctuating limiting resources. They simulated the dynamics of 300 competing species, which were assigned different trait values along a trait gradient. Here the traits were the half-saturation coefficients for the 2 limiting resources: these were related via a tradeoff between the half saturation constants for each resource.

What they found is strikingly similar to the results from Scheffer and van Nes and dissimilar to the the results that emerge when resources are constant. Clumps of coexisting species emerged along the trait axis. When resource fluctuations occurred rapidly, only fairly specialized species survived in these clumps (R* values that were high for either resource 1, or resource 2, rather than intermediate). But when fluctuations were less frequent, clusters of species also survived at intermediate points along the trait axis. However, in all cases the community organized into clumps composed of very similar species that were coexisting (see Figure 3). It appears that this occurs because the fluctuating resources result in the system having non-stationary conditions. That is, similar sets of species can coexist because the system varies between those species' requirements for persistence and growth. 

Fig. 3. "Lumpy species coexistence". The y-axis shows the trait value (here, the R*) of species present under 360 day periodicity of resource supply.  

Using many of the dominant models of competition in ecology, it is clearly possible to explain the coexistence of both similar or dissimilar species. This is true across approaches from the Lotka-Volterra results of Scheffer and van Nes, to Tilman's R* resource competition, to Chessonian coexistence (2000). It provides a unifying expectation upon which further research can build. Perhaps the paradox of the planktons is not really a paradox anymore?

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Reference:
Athanasia Sakavara, George Tsirtsis, Daniel L. Roelke, Rebecca Mancy, and Sofie Spatharis. Lumpy species coexistence arises robustly in fluctuating resource environments. PNAS 2017; published ahead of print December 20, 2017, doi:10.1073/pnas.1705944115

Some of the best advice on the internet: several years of links

I started off the New Year with a much-needed bookmark reorganization and deletion, which also gave me a chance to re-read some of the links I've held onto (sometimes for years). There's an ever-increasing amount of useful content on the internet, but these have proven some of the most helpful, concrete, and lasting guides for navigating a scientific life.

I thought I'd collate the list here with the hope others might find some of these useful.

How to make it as early career researcher and new faculty: 

• There's no one way to be successful (in fact there are many ways), but hearing what not to do can be as important as all the other advice you will get: Mistakes I've made as an early career researcher (Times Higher Education)
• Lots of what-to-do and what-not-to-do advice here! How to be a new professor (Eco-Evo Evo-Eco)
• It's hard enough that Berkley's career centre has written pages on the topic: Transitioning from PhD to Prof (Careers at Berkley)

Identity and academia:

• I think most of us took different and often interesting routes to science (for example, I grew up in an evangelical Christian family, took a number of years to finally start my undergrad, and had no particular knowledge of ecology when I started my BSc. I wanted to be a vet, but now I'm an ecologist. Close enough :) ) and so I like to hear the many different routes by which scientists found science (SEAS).
• Overcoming imposter syndrome - there are many websites devoted to the topic, but this one provides particularly concrete steps to overcoming this common problem. 
• No one is perfect, and feedback can hurt - why feedback hurts and how to over come that. And no, it isn't enough to say, 'grow a thicker skin' (The Thesis Whisperer).
• Diversify EEB - a useful list of women and minorities working in EEB, worth keeping in mind when making nominations, selecting reviewers, and making various invitations. 
• And it's worth remembering that there is a dark side (one slightly bitter take on it). (Fear and Loathing in Academia)

Mentoring and leadership:

• Mentoring plans - this is super-useful whether you are faculty or a student. In a lot of places there may not be a tradition of having written plans and this provides examples of how to put them into practice. (Meg Duffy, Dynamic Ecology)
• Running successful lab meetings from all perspectives. I like the idea of explicitly deciding what you value and then modelling that when leading lab meetings. (Neuromechanics Lab at Emory)
• Discussing and leading successful journal clubs especially for graduate students.  (Tulane University, Dept of EEB) 
• Running successful scientific working groups and workshops (Conservation Bytes) 
• Preparing PhD students for non-academic careers (The Versatile PhD)

Computing/Data management:

• How to organize your computational projects (PlosBiol)
• Ten commandments for good data management (Brian McGill, Dynamic Ecology)
• Proselytizing about version control (Jennifer Bryan, Peer J)
• A list of lists of links for R statistical resources on Google Documents  
• Data sources worth checking out at The ecological data wiki

Data visualization:

• There are some really beautiful infographics about science from Eleanor Lutz here (Tabletop Whale).
• Information is Beautiful - infographics for inspiration
• Show me Shiny - some great examples of how R Shiny has transformed data visualization and interaction.
• If you are familiar with Edward Tufte's influential work on data visualization, you can use R to produce similar plots here. (Lukasz Piwek)

Teaching:

• The Eco-Evo Evo-Eco blog has a great how-to series, but this one especially has really concrete examples and suggestions for EEB lecturers: How to Teach (Eco-Evo Evo-Eco). 
• An article on how to use new learning methods for quantitative biology and models at Active learning and quantitative biology (Integrative and Comparative Biology)  
• The National Centre for Case Study Teaching in Science for already developed case studies and activities. 
• Common mistakes using active learning - nice to see legit critiques and ways to improve.
• Smart advice on avoiding bad teaching reviews (Terry McGlynn, Small Pond Science)

Miscellaneous links:

• Now part of the PhD canon: The infamous Snake Fight for your thesis to distribute to your PhD students (McSweeney's Internet Tendency).
• Wall of the Dead - poignant memorial list of ecologists lost while working  (Richard Conniff).
• The original text for Gause's The Struggle for Existence and plots. Ecology has changed so much (and so little).
• Visualize the tree of life beautifully and interactively (and get your name on it (sort of)!) (One Zoom)