Cuentos-Contos Week 3

For the final week of Cuentos/Contos, I present Dr. Bryan Juarez, Dr. Stepfanie Aguillon, Dr. Raul Diaz, Kiersten Formoso and a special interview with Dr. Melissa Guzman. 

Southeast LA and San Bernadino born and raised, Bryan, describes how his low-income background drove him to design novel mathematical approximations to tackle complex science problems (jumping in frogs), as an alternative to using expensive equipment that may have been financially inaccessible. He also explains how his Latinx background prepared him to spot genuine mentors and allies, which have now blossomed into solid friendship. Finally, Bryan touches on experiencing culture shock as a Latino in academia and how EEB departments can support their fellow Latinx academics. 

Stepfanie, a Texas-born, but Arizona-raised evolutionary biologist, tells us about how her immigrant roots influenced her work and school ethic. The importance of building community is discussed, especially with allies that understand DEI issues and the type of mentor she strives to be. Lastly, Stepfanie offers three important recommendations for making academic cultures more inclusive for Latinx students. 

Fervent herpetologist, Raul, remembers reading books on amphibians and reptiles at the local public library and knowing from childhood that her has going to pursue a career in herpetology. He also explains how his parents paved the way for success and supported him, despite not understanding his research interests. Lastly, Rauul argues that a diverse faculty team is th best way to attract Latinx students. 

Kiersten, a New York born but Jersey-raised vertebrate paleontologist passionately describes her reserach exploring land to sea [not sea to land!] evolutionary transformations. She tells us how she shakes off heightened self-awareness (as one of the few Latinx in academic spaces) and addresses barriers that keep Latinx from pursuing science careers. Despites these setbacks, Kiersten has also had many positive expereinces and ramains inspires to pursue a career as a tenure-track professor. 

Check out this special interview with Dr. Melissa Guzman!

To learn more about the featured scientists reach out via their emails or websites,

Bryan - Bryan H Juarez (bhjuarez.github.io) 

Stepfanie - Stepfanie M. Aguillon, PhD – Stanford Science Fellow (stepfanieaguillon.com)

Raul - The Diaz Laboratory – "We Study Weird" – Evolutionary Developmental Morphology of Reptiles (reptile-embryos.com)

Kiersten - Home & Blog | Formorphology

Dr. Melissa Guzman - Melissa Guzman |Ecological Data Science Lab (edslab.org)

To check out the full versions of all the Cuentos-Contos, follow this link. 

I hope you all can take away something from reading the cuentos/contos of so many brilliant scholars and people. Although I only shared the cuentos/contos of 13 Latino/a/x scholars, remember to share your own and tell your cuento/conto. You never know who is reading, and who will be the next, Ecologist, Developmental Biologist, Evolutionary Biologist, Astrobiologist, Microbiologist, Marine Biologist, Paleontologist, etc inspired by your cuento/conto. 

Cuentos-Contos Week 2

In week 2 of the Cuentos/Contos, I am pleased to share the cuentos of Daisy Flores, Eduardo Tassoni Tsuchida, Alonso Delgado and Maya Yanez. 

Daisy Flores, a San Diego local and marine biologist, tells us how her Latinx identity has influenced the way she approaches education in the US and abroad. Additionally, Daisy emphasizes how a strong support system encourages her to preserve in academia, even during tough times. Lastly, she provides a few suggestions to increase inclusivity in universities and departments. 

Eduardo, a biologist studying cell response to stress, reflects on how his Brazilian background has shaped his grad school experience. He touches on the importance of therapy and keeping up with family during the COVID-19 pandemic. Eduardo believes he is doing his part to increase Latinx representation by mentoring Latinx student and working in various programs and committers to promote inclusivity and community. 

Alonso (originally hailing from the San Fernando Valley), shares his academic evolution: from pursuing an aviation adminstration degree in community college to obtaining a BSc. to currently researching venom changes in off-sea anemones! He also discusses the hidden curriculum, mentorship in academia and why he started organization, "Latinx in Marine Sciences".

Geobiologist and Los Angeles local, Maya Yanez, recounts navigating academia as a first-generation scholar, including the terrifying moments when she found out her loans were denied and how the problem was resolved! She explains how acknowledging and embracing her identity as a Latina has shaped her academic career. Maya candidly addresses her plans for the future, the reasons why she is not considering a career in academia and suggestions on welcoming and retaining Latin students. 

To learn more about the featured scientists reach out via their emails or websites, Eduardo Tassoni Tsuchida - etassoni@stanford.edu, Maya Yanez - Mdyanez@usc.edu, Daisy Flores - dmflores@utexas.edu, and Alonso Delgado - Home | Alonso Delgado (delgado73.wixsite.com).

To check out the full versions of all the Cuentos-Contos, follow this link. Don't forget to view last week's cuentos/contos. 

And for now, Hasta luego! 

Cuentos-Contos Week 1

 In the Fall of 2021, I sought out to highlight the stories of Latino/a/x researchers generally in "Ecology and Evolution". With the support of Dr. Carly Kenkel, Dr. Oliver Rizk and Emily Aguirre, we were able to put on a series titled "Cuentos-Contos" (short stories in Spanish and Portuguese) to be shared to our Marine and Environmental Biology section at the University of Southern California. Now I am sharing these short stories to Ecoevoevoevo to share with a wider research community as I believe it is important to highlight researchers from similar backgrounds in academia as this space is often isolating. Throughout the next couple of weeks, I hope you feel inspiration, empathy and joy as every person details how their career, passion and aspirations intersect with their culture and identity. To start off, I highlight several talented scientists: Emily Aguirre, Ivan Moreno, Melody Aleman, and Dr. Suzana Leles.

Angeleno microbial ecologist, Emily, studies algal-bacterial symbiosis in the emerging cnidarian system, Aiptasia pallida, using genomic, culturing and microscopy techniques. In this cuento, she highlights her support system as a "non-traditional" student and discusses inefficient, outdated and harmful academic structures. Emily concludes by suggesting solutions for improving the academy, and transforming it into a space that truly supports talent, ingenuity and diversity. 

 Long Beach raised microbial ecologist, Ivan, studies microbes in extreme environments via genomics. He discusses the importance of a work-life balance as an underrepresented student (soccer and video games!) and how this keeps him grounded. Ultimately, Ivan believes that if he strives to be the best scientist and researcher now, he will be able to provide those same opportunities for others once he's an established academic.

Pennsylvanian marine microbial ecologist, Melody, breaks down the racial, social and class obstacles she faced as a Latina, on her way to grad school. She addresses the importance of utilizing the university's mental health resources, and how this has helped her cope with the global pandemic and anxiety. Lastly, Melody gives a shout out to her former/current mentors and encourages departments to support their Latinx students through more funding opportunities and access to genuine mentorship.

Brazilian oceanographer, Suzana, discusses he academic trajectory across the globe, and how she ended up building mathematical querying microbial food webs in Los Angeles, California. She addresses the supporting factors (and discouraging aspects) that allowed her to succeed and become a Ph.D., despite enduring hurtful experiences and how she continued on an academic track. Finally, Suzana provides helpful tips on building welcoming spaces for non-native English speakers!

To learn more about the featured scientists reach out via their emails or websites. Ivan -  imoreno[at]ucsd.edu, Melody - maleman[at]usc.edu, Suzana -  suzanaleles[@]gmail.com, Emily - Emily Aguirre (weebly.com). 

To check out the full versions of all the Cuentos-Contos, follow this link. 

And for now, Hasta luego! 

Within, among, or between?

I was recently surprised to learn from my students (I really appreciate that they spoke up about it) that some phrases I had been using were confusing - most specifically among-population variation.* This led to a discussion of the meaning of within, among, and between and how these terms are used in ecology and evolution (a microcosm of how they are used more generally). As the confusion appears to be more common than I thought, perhaps it is worth explaining the situation here.

To make this explanation clear, first imagine that you are analyzing a number of separate populations (e.g., humans in different populations) and that you have measured a particular trait (let's say body size) in a number of individuals in each of those populations. Note: this is not a random example, it is precisely what we did in a paper some years ago - McKellar et al. (2009). Below is a figure from that paper providing compilation of within-population variation (y-axis) and among-population variation (x-axis) measures (here the "coefficient of variation" - CV) within a large number of animal populations.

Within 

If you report a descriptive statistic for each population separately, those measures are within-population summary statistics. Thus, within-population variation is a measure of variation within each of those populations - as might be indexed in a variance or standard deviation or coefficient of variation of body size for each of those populations separately. You can then also calculate the average or variation (across populations) of those within-populations measurements. In this case, you use the various within-population measures you have calculated (e.g., the variance within each population) as data to calculate another set of descriptive statistics, such as the mean (across populations) of the within-population variance. 

I should note that, in some cases, one wishes to assume that these within-population measures are all estimating the same global (that is, shared across populations) within-population variance (or mean or whatever). In such cases, it can be assumed that populations with larger sample sizes (more individuals measured) are providing better estimates of that shared (common across populations) within-population parameter - and so the estimated average (across populations) of the within-population parameter is calculated by weighting the within-population estimates by their sample sizes. This is precisely what is done when one calculates a "pooled standard deviation." Of course, variation among the within-population estimates of the parameter are a measure of how much variation might exist among populations in those within-population parameters.

Between and Among

If you next report a descriptive statistic that examines trait variation across the populations, then you are in the world of between-population (if across only two of the populations) or among-population (if across three or more populations) estimates.** Typically, these estimates do NOT include variation within those populations. That is, you don't simply pool all of the individuals across all of your populations and calculate a single mean or variance - because this approach mixes within and among population variation.***

So, instead, the simplest approach is to take the within-population parameter estimates, such as the within-population means and variances of trait values for each of populations you measured, and use them as data points to calculate a new mean and variance. The first of these (the mean of the means) was mentioned above as it is the mean of the within-population means - and thus the "best" estimate of the trait mean within populations (assuming they are the same - or near enough as to make no difference). The second of these (the variance of the means) is a measure of among-population variation - that is, it is the variance among population means. It is the among-population variance.

Of course, the within and between population contributions to variation can be estimated together from an appropriate statistical model (e.g., nested analysis of variance) that appropriate partitions the variance between the different levels. Further, uncertainty associated within lower levels of the hierarchy (e.g.. variance within populations) can be propagated in some models (e.g., Bayesian) up to higher levels of the analysis (e.g., variance among populations).

I above noted that estimates of among-population variance should not include within-population variance. However, some analysis are interested in scaling the among-population variation by the within-population variation. The simplest way to do this is to divide the among-population variance by the within-population variance - and versions of this are seen in the estimation of parameters such as FST, QST, and PST.

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* When used as an adjective preceding the noun, you want to use a hyphen (e.g., within-population variation) but, in other situations, you don't want to use a hyphen (e.g., the variation within populations). 

** The terms "between" and "among" are also used more generally in writing when you are discussing analyses that are contrasting only two populations (between) or when you are contrasting more than two populations (among).

*** As an aside, this is one of the issues encountered when performing PCA on data from multiple populations simultaneously. That is, PCA (as opposed to DFA) ignores population identity and thus generates axes that combine within-population and among-population variation, which can generate considerable biases. Note: I am not saying PCA can't be used in such instances - but rather that it should be used with caution.

SLiM 4: Multispecies eco-evolutionary modeling (a personal history)

Once upon a time, I did my PhD with Andrew Hendry at McGill.  My PhD involved writing individual-based evolutionary models of various sorts, to look at things like local adaptation, adaptive divergence between environments, and speciation.  Each model I wrote for my PhD was bespoke – a custom model, with custom C code to simulate what I wanted to look at for a given project.  (I did write a general-purpose modeling environment within which I implemented each of these bespoke models, which provided graphical visualization of the running models for me; but the models themselves were each coded by hand.)  Each model would have its own parameters, governing things like population sizes and migration rates; each would have its own implementation of some sort of genetic architecture; each would have its own approach to selection and fitness.

But now and then, Andrew would get a gleam in his eye, akin to the gleam in Gandalf's eye when he smoked his pipe and talked of strange lands and great heroes.

Gandalf's gleam in the eye

Of course Andrew would be sipping whiskey, not smoking a pipe!  And when Andrew was lost in these distant thoughts, he would sometimes speak of "One Model to rule them all".  One Model to rule them all, One Model to find them, One Model to bring them all, and in the cluster... simulate them.

The One Ring Model

What he meant, of course, was that it would be great not to need to write each new model from scratch; it would be great to have one "uber-model" which could do everything, and then each particular model that one wanted to explore would just be a particular parameterization of that uber-model.  Concepts like "migration", "selection", "population structure", and "genetic architecture" are – one could argue – general concepts that you would like to be able to code once and reuse, over and over.  Once the uber-model was written, you would never need to write a model again.

In its pure form, this idea is obviously a pipe dream; one could never write an uber-model that is so flexible, so general-purpose, so Platonic, that every other model one could imagine is just a shadow cast by the uber-model upon the cave wall.  It's an attractive vision, but there's no way it could ever be real.

And yet the idea stayed with me.  Perhaps not an uber-model, as such... but perhaps a modeling framework.  Perhaps one could write a modeling framework that would provide lots of tools and utilities, building blocks for model-building.  Writing any particular model could then just be a matter of glueing together the provided building blocks.

After my PhD, I started working with Philipp Messer at Cornell University.  Philipp had written a population-genetics simulator that he named SLiM, and he wanted somebody to improve it and maintain it.  Since 2015, I've been chugging away at improving SLiM, step by step.  It now provides a cornucopia of building blocks, for everything from genetics to spatial modeling; it provides a scripting language called Eidos with which you can glue those building blocks together in whatever way you wish; and it provides a graphical modeling environment in which you can write your Eidos scripts, run them, and see the resulting evolutionary dynamics visually as the model runs.  It's pretty widely used in population genetics (SLiM 3, SLiM 2, SLiM 1), and has enabled a lot of cool research.

SLiM's icon, with a tip of the hat to Piet Mondrian

But SLiM hasn't really been used as much as it could by people interested in evolutionary ecology, in eco-evolutionary dynamics, in predator–prey systems and host–parasite systems and things of that sort.  The reason is that SLiM didn't really have much ecology.  It started out as a population-genetics simulator and it stayed in that world for a long time.  You could simulate a biological system from the level of mutations, to genes, to chromosomes, to individuals, to subpopulations, to a whole species; but you couldn't really model more than one species, and the interactions between those species, and the coevolutionary feedbacks driven by those interactions.  So it remained a tool mostly for population genetics.

I am very pleased to announce that that era is over!  SLiM can now model evolutionary ecology: multiple species, interspecies interactions, coevolutionary dynamics, and eco-evolutionary dynamics.  It now spans the biological hierarchy from individual mutations up to not just a species, but a whole ecosystem or even a community.  I'm really, really excited to see what folks do with this; for me, this is the realization of more than a decade of dreams.

Support for multiple species was added to SLiM 4, which was released on 12 August 2022, so it has actually been available for a little while now.  I put off writing a blog post about it here until the corresponding paper was in the publication pipeline... and now it is, in the American Naturalist.  The title is "SLiM 4: Multispecies eco-evolutionary modeling".  At present you can download the paper in its "just accepted" form; it hasn't been typeset yet.  Here's the DOI: https://doi.org/10.1086/723601.

I'm not going to say anything more about SLiM 4 and multispecies modeling, because, well, that's what the paper is for.  Of course this is not the end of the journey.  I'm sure there are lots more building blocks that will need to be written to make multispecies modeling as flexible and general as we want it to be; and there are lots of other projects too, from improving and generalizing SLiM's genetics to making SLiM run faster by utilizing multiple processors.  But ever since I started working on SLiM, my primary end goal for it has been to turn it into an ecosystem simulator – really, to try to bridge the gap between population genetics and evolutionary ecology by making it possible to simulate both in the same model.

And if this obsession with the dream of the One Model has consumed my life a bit, and turned me into a troglodyte that flinches away from the sun, well... it has all been worth it for my preciousss.

The author, with a fish.

Grammar tips/rules for scientific writing

In my roles as supervisor, collaborator, reviewer, and editor, I read many scientific papers in draft (pre-publication) form. When reading, my hope is always to concentrate on the science itself - and how well it is communicated. Sometimes, however, I get stuck on particular grammatical errors and find myself repeating again and again and again various grammar "rules." I provide a listing of them here in hopes that they are picked up, used, and propagated just a bit more than at present.

1. Avoid long, complicated compound sentences. These are often very difficult to follow.

2.     Use “which” and “that” properly. “That” should be used for restrictive clauses (“This is the fish THAT Jack caught.) whereas “which” should be used for nonrestrictive clauses (“This fish, WHICH Jack caught, is a salmon.”) Most people use “which” in many cases where “that” is more appropriate.  

3.     Avoid all use of “there is”, “there was”, “there are”, and “there were”, particularly at the start of sentences. Use of these terms can make the subject of the sentence unclear.

4.     Avoid unnecessary amplification of text. For example, say “sneaky mating is successful” rather than “sneaky mating has been found to be successful”. 

5.     Avoid the use of “while”, except when the intended meaning is “during the time that.” In other contexts, “whereas” or “although” are usually better.

6.     Write out all numbers less than 10 (i.e., one, two), unless the number is followed by a unit, such as m, mg, min, h, etc.

7.     “Data” are plural. That is, you don't say: "the data is", you say "the data are." Datum would be the singular version.

8.     “Between” is used in reference to two things. “Among” is used in reference to more than two things. That is, you study the differences between two populations, but the differences among three populations.

9.     Never use “etc.”

10.  Never use “unique” unless you truly mean “one of a kind.” People often say: “Our system represents a unique opportunity to test the theory that…” Instead, say: “Our system represents an excellent opportunity to test the theory that…” Similarly, never use “ideal” or “perfect” in this same context.

11.  My Mom (a grammar expert of sorts) tells me that only God “creates” things (and she isn’t even religious). So, in short, don't use the term create unless you are invoking God.

12.  Strive for parallelism between related sentences that appear close to each other. As a simple example, use “Low predation sites are characterized by few fish predators. High predation sites are characterized by many fish predators.”, instead of “Low predation sites are characterized by few fish predators. Many fish predators are found at high predation sites.”

13.  Beware of misplaced modifiers. For example, “We measured body depth using calipers.” Body depth does not use calipers, as this sentence implies. Instead, use “We used calipers to measure body depth.” Sometimes it is difficult to avoid misplaced modifiers without otherwise destroying the sentence. In such cases, it is forgivable.

14.  Use the active voice (“We measured body depth.”), rather than the passive voice (“Body depth was measured.”), whenever reasonable and when not explicitly disallowed by a journal. Be careful to not use it too much though. Six sentences in a row, all starting with “we”, are very awkward.

15.  Although many would disagree with me, I believe in the power of punctuation. As one small example, I believe the second last phrase in a list of phrases should have a comma before the “and.” For example, “Speciation can occur by genetic drift, mutation, and natural selection.” rather than “Speciation can occur by genetic drift, mutation and natural selection.” Using the latter often introduces confusion when the phrases themselves are longer and contain “and” within them. The cartoon gives another example:

16.  Always use a single space between sentences. All journals do this anyway, and it makes editing difficult if one person (me) uses single spaces and other people (you) use double spaces.

17.  Try not to use “may” unless you are implying permission. Instead consider “might” or “can”.

NSF Postdoc Fellowships

The following is a guest post by Dr. Alli Cramer, at the University of Washington. @AlliNCramer

How do NSF postdoc proposals work, anyways? 

Since the Ocean Sciences Postdoctoral Research Fellowship (OCE-PRF) has just been announced, it seems like a good time for a quick discussion of how to apply, or how to begin thinking of applying for NSF postdoc fellowships! Many of these are due in early November so as of September prospective postdocs have about 10 weeks to refine their projects. This is a modification of a twitter thread I wrote a year or so ago, but it does have some extra information if you’ve already seen it. 

My experience applying for PRFs comes from applying for the ‘Postdoctoral Research Fellowship in Biology”, PRFB, in 2019 and 2020, and applying for the OCE-PRF in 2021. With that as my background, some of this advice will be program specific, but much of it is some of the ‘unwritten rules’ of NSF so hopefully it can be helpful to other fellowships as well. Ultimately,  I was funded on my 3rd attempt at an NSF postdoc and getting to that point was quite a learning curve. In particular, I didn’t know what to expect regarding timeline or paperwork.

Proposal Preparation

First, and definitely the most important -  connect with Program officers (or Program Directors - seems to vary by division). Do this as early as you can, and feel free to check in with them multiple times: their names are listed on the NSF website for your specific proposal. As a graduate student it can be intimidating to reach out to Program officers, but you should 100% email them and discuss your proposal idea. It is the job of Program Officers to help you make sure your proposal fits the brief of the solicitation before you submit it. They can also answer questions you have about formatting or paperwork. For proposals due in November, contact them now to start refining project ideas.

While drafting the proposal attend the Q & A session(s). These are important to clarify solicitation language and answer questions you didn't know you had - You don't want a proposal rejected because of a formatting error! Make sure to go to the session or get detailed notes from someone who did. The Q & A session dates are listed on the NSF page for your proposal. Sometimes these are also listed on the solicitation (the hella long HTML page with all the specific language) but not always. They’re normally listed as important dates on the website that links you to the solicitation itself. As of this blog, some programs now offer Office hours - these are great places to get questions answered and connect with the Program Officers (double whammy!).  

Like the Program Officers, the IT at NSF is an excellent resource for you. Proposals are submitted through an online portal (currently Fastlane, though that is changing). If you have questions or if something isn’t working, reach out to IT. I had computer issues uploading a proposal and they responded fast and fixed the problem.

Because of the jankiness of the upload portal, upload drafts of your proposal early. Like, two or three days early. Every time that I submitted proposals I tweaked files up until the deadline, but I made sure I had a good enough copy of each file uploaded a few days before. This was useful because it let me see what wasn't working (and led me to contact IT). 

When you’re writing your proposal, in addition to the description of your project and the budget etc., you will need to have letters of support from your potential mentors. Make drafts of support letters for mentors that they can work from. Mentors can use your draft as a springboard and rewrite it, but your draft will help them understand the role they have in your project more clearly. Writing it out for them not only saves time, but forces you to be explicit about your mentorship goals and needs. 

After submission 

After you submit your proposal the earliest you can expect to hear back is ~ 3 months. If your proposal is recommended or declined, you should hear around the same time. If you haven’t heard anything by then it doesn’t mean you weren’t funded, but it doesn’t mean you were. There is always a batch of proposals that NSF would like to fund, but that are low on the priority list. If you can resist, avoid constantly refreshing the status page 😛If you haven’t heard back & have deadlines looming (accepting job offers, etc.), reach out to the Program Officer. They are super helpful & responsive - they helped advise me when my proposal was in limbo, even when everything was a mess due to COVID shutdowns. They can’t tell you if your proposal will be funded, but they can give you insight into timelines, etc. 

If your proposal is selected, you will hear back over email - make sure to check those spam folders. You will need to send back paperwork to accept the award. In my experience, this has a tight deadline (less than business 5 days) so you will need to work fast.

The paperwork involves coordinating with your host institution and NSF. You might need to get a version of your proposal through the institution’s research grant office. Get in contact with your host institution’s department’s coordinator/grant manager/director because they are the experts. 

For my proposal I also needed to draft a letter “concurring with the transfer of the award to the host institution.” I couldn’t find any examples of those online, but I drafted one up using the standard business style letterhead. My letter went like this: 

Dr. Allison Cramer

[Home address]

[Phone number]

[Program Director]

[Fellowship title]

National Science Foundation

2415 Eisenhower Avenue, 

Alexandria VA 22314

[date] 

To the [fellowship name] Program, 

This letter concurs with the transfer of Proposal ID ##### [proposal title] to the primary host organization, [institution name]. 

Sincerely, 

[signature block]

After the letter and all the other paperwork is sent back there is another batch of waiting. During this time your proposal status page might not change, and the only “proof” you have that you got funded is re-checking your email compulsively. After a few weeks the proposal status shifts to Recommended & a few days later you will receive emails that your proposal is being funded and the status changes to Awarded. Some of these emails are auto generated so have weird subject lines (so check spam folders).

Proposal Feedback 

Whether it was funded or not, after you hear back you will get feedback from proposal reviewers. This feedback includes a summary and individual reviewer thoughts about your proposal. The summary of proposal reviews is most important - it synthesizes individual feedback to highlight what matters. For example, one reviewer for my funded proposal found aspects of my proposal unclear in their written feedback; in the summary however this wasn’t mentioned at all. The other two reviewers understood that part of my proposal, so it was hashed out among the reviewers in the in person discussions they had. In contrast, on one of my unfunded proposals two reviewers highlighted a gap, and that gap was again emphasized in the summary feedback. This let me know to focus on it for my next attempt (the successful one!).

Here I am going to plug Program Officers once again. You can contact them about these reviews and they can help you make sense of the feedback. They are ‘in the room’ when the discussions happen, so can help identify what to prioritize for revisions should you resubmit. In general, postdoc proposal or not, contacting program officers is good practice for any researcher looking for NSF funding. It is essential for connecting with NSF programs, and for parsing solicitations. NSF wants to fund good science. The Program Officers help researchers frame their questions and put their best proposals forward. 

All of the above info is my experience with NSF. If you have questions about being in this strange postdoc stage, feel free to connect with me on twitter @AlliNCramer. You can DM me and I can point you in the right direction. Good luck to all of you writing those postdoc proposals!