Drawing on my work in evolutionary ecology, wilderness ethics, and machine learning, I reflected on the tension between our desire to intervene and our limited ability to forecast long-term ecological outcomes. Using examples like the Chernobyl exclusion zone—where many species are thriving in the absence of people despite nuclear contamination—I argued that ecological recovery is often less about precision intervention and more about restraint. We discussed how machine learning can help us simulate alternative futures and understand potential tradeoffs, but that ultimately, the most powerful conservation tool may be humility. More wilderness, not more control, might be the best way to meet the uncertainties ahead.

Listen to the episode here or wherever you get your podcasts.

Today it is trivial to make really sexy graphics in a few clicks and keystrokes. But the ease of creation also makes it much easier to produce poorly planned or spurious outputs. It also means that the marketplace of people’s attention is now flooded with loads of other eye-catching data visualizations to compete with.

Now, more than ever, it is important to think strategically about how to present your work. This blog grew out of a guest lecture I gave. It is intended to present some conceptual tools to help you make your data stand out.

To data viz or not to data viz?

Making a stellar data visualization takes time and effort. Even a simple plot for a scientific paper can take a while to get to the final print-ready stage. So, for starters, it is worth considering just how much time your particular data viz project is worth.

It is really easy to go deep into a rabbit hole making a beautiful visualization, or even an entire data storytelling project, only to have it sit on your computer or collect digital dust in some dark corner of your blog. My most adamant piece of communications advice is that you should spend just as much time planning how to outreach your work as you do creating it. And after you’ve got your product, you should again spend the same amount of time actually making sure that people see it. (This rule applies less to journal figures since the outlet is predetermined, but you should still plan to spend as much time sharing your hot-off-the press manuscript with your stunning figures after it comes out.)

Data visualization or data storytelling?

When people think about great data visualizations, they often think about the flashy and interactive products like those from the Washington Post or New York Times. I also love these interactive visuals, but to me, they are something more than data viz–they are data storytelling. Rather than simply displaying data, data storytelling integrates data as a part of a larger narrative. Good data storytelling involves skills that overlap with data viz, but add much more. For instance, my friend Collin’s Story Map of his research on lizards evolving to hurricanes is a great example. We learn all about his research and how he produced his data, but very little about the data itself.

In this article, I want to focus narrowly on data viz and how we interpret statistics visually. There are loads of plot forms that you can use, and folks are always coming up with new ways to use them, so rather than create an exhaustive list, I want to consider when and how we use data visualizations.

 One quick caveat here: data viz implies that the only way to interpret data is with sight. But there are some really cool projects that display data without visuals, like my friend Lauren, who translated Alaskan tree loss through sound.

Grabbing your attention or focusing your attention?

One of the first questions to ask yourself in defining the purpose of your visual is: am I trying to grab folks’ attention or do I want to focus their attention? Humans brains are not all that well designed for sustained attention (I go in depth about this in my presentation about scientific presentations), so most of our task as science communicators is simply managing people’s attention spans. Flashy and interactive visuals are great for catching your audience’s eyes, but can be a distraction from carefully interrogating specific trends in data because there is too much to focus on. On the flip side, an equally beautiful but more subdued plot can perfectly highlight a specific point you want to make about your data, but folks might flip or scroll right past it if they are not actively interested. Considering your audience is paramount. For example, in a paper, I may include lots of information in a plot, but when I present my work in presentation form at conferences, I completely strip down my figure to their most basic elements.

One of the reasons we have short attention spans is that our brains have evolved to process lots of information quickly. As a tradeoff, our brains take cognitive shortcuts. If we are clever, we can use visualizations to hack our brains and leverage those shortcuts. As an example, take a look at the two images below. Can you tell which image of stars is randomly placed? Can you tell which set of numbers is random?

Human brains are overly tuned to seek patterns. Often, we see patterns when none are there (maybe this is where human predilection for superstition, conspiracy theories, and religion come from). Most people think that the blue stars (B) and number string A are the random sets. That is because we tend to see too much pattern and clustering in the black stars and too many patterns of repeats in number string B. When we see patterns, we assign meaning. In fact, the black stars are randomly placed (the blue stars are overly uniform) and number string A is randomly generated.

This is convenient for data viz, because it makes it easy for us to see trends in complicated data. For example, when Nature plotted all of it’s published papers over the last 150 years, and then linked them by citations, the result was incredibly complicated. But our minds tune-out most of the noise and instead focuses in on the major groups where fields merge. 

On the flip-side, our minds are quick to spot deviations from patterns, too. For instance, when Campbell et al. plot coding density versus genome size, it is easy to spot the clade of endosymbionts (in green) that deviate from the trend.

Our brains are also really bad at conceptualizing large numbers. For instance, if I told you that humans have about 3.2 billion bits of information in every cell of your body, but E. coli has just 5 million, and Paris japonica flower has almost 150 billion, the scale might be hard to grasp. But if I compare your genome to the letters in an encyclopedia and visualize the difference, the disparity is clear.

Making visuals that strategically hack our brains.

When it comes to visuals, I don’t like prescribing rules. Aesthetics change too quickly. Instead, I think it is more helpful to be strategic about the content of your visuals and treat the aesthetic refinement as an artistic process. 

Scott Berinato’s book Good Charts comes from the perspective of management rather than science, but is, nonetheless, one of the best examples I’ve found of thinking strategically about making visuals. Berinato thinks that visuals fall on two intersecting gradients: Conceptual versus Data-driven (are you dealing with ideas or statistics?) and Exploratory versus Declarative (are you looking for a pattern or are you showing a pattern?).

1. Everyday data viz

Usually, when we think about data viz, we are thinking about graphics that fall into the upper right quadrant, data-drive declarative graphics, what Berinato calls “Everyday data viz.” The purpose of these graphics is to highlight specific facts about our data. Most of the figures from scientific papers fall into this category.

Within the “everyday data viz” category, there lies a wide range of visualization goals that depends on the intended audience. For example, I made a mirrored radial barplot comparing population density to tree cover. Wrapping this plot into a radial form makes the data more interesting, but actually makes it more difficult to read. If I were to include these data in a scientific paper, I would probably use a dotplot like the one in the top left of the figure. The dotplot displays the same information in a way that is more conducive to quantitative comparison.

With these types of visuals, there is often a tradeoff between simplicity and aesthetics. Usually, simpler is better for scientific audiences. However, sometimes the whole point of a graphic is to demonstrate complexity or variation in the data. For instance, a simple mixed model regression could be easily displayed as a single trend line.

Not only is this super boring, but it misses one of the points of mixed models, which is how we deal with variation in the data. Below are six examples showing the same trend while highlighting the variation in the data in different ways.

On the other hand, when giving scientific presentations, we want to highlight the main trend without distracting the audience with noisy variation. In a prior post, I used the fake example below, where the most important trends (bottom figure) are completely buried in the meaningless distraction of too much information (top figures).

My main point here is that you must be strategic about who your audience is and exactly what you want them to take away from your visuals. It is unlikely that anyone will think as carefully about your graphic as you have. Instead, most folks will take away a fraction of the information you present. So, it is worth being as parsimonious as possible with the content in your graphics. One tip for presentations is to step away from your computer and squint your eyes–if you can’t make out the main trend, you probably should strip it down. Another tip is to start with the bare axis and explain them to your audience before showing the content of the plot. This way, they already know what to expect and they will not be as distracted trying to conceptualize what the graphic is saying.

2. Visual discovery

The graphics in the upper right quadrant of Berinato’s diagram are like the perfected Pintrest versions of our visuals. Before we get to that point, we will probably plot a ton of graphs as we analyze our data that no one ever sees. Berinato calls these graphs “visual discovery.” They fall in the lower right quadrant of data-driven exploratory plots. 

As we explore our raw data, it is useful to hack our own brains to discover hidden patterns in our data. Most data is multidimensional and too complex to see every relationship at once. So, we check for relationships among variables and among subsets of variables. This process is usually iterative. The point isn’t to make perfect, pretty graphics–the point is to wrap our minds around the data.

One of my favorite examples of visual discovery involves one of the oldest examples of data viz. 

In the mid 1800s cholera was sweeping into London. At the time, few understood how the disease was transmitted. John Snow (no, not that John Snow) a medical doctor decided to plot the cases as bar charts of the number of victims at each address on a street map of the city. The map showed a public well at the center of the epidemic. The map helped Snow convince skeptical municipal authorities to close the well and effectively ended the outbreak.

Visual discovery is what scientist probably spend 80% of their analysis time on (I certainly do). Plotting programs like Rstudio or MatLab (and to a lesser extent, Excel) make it really easy to play with lots of ways to see our data and easily iterate to narrow in on interesting trends.

3. Idea illustration

The top left quadrant, conceptual and declarative, Berinato calls “Idea illustration.” These are usually heuristics, flow charts, or diagrams with the purpose of visually demonstrating a complex idea in picture form. Scientists use these type of graphic often in review or synthesis papers. For example, I made the figures below for a recent review paper of herp thermal evolution. Neither are based on data. The first demonstrates a theoretical process. The second illustrates what real data might look like and how to interpret them. These types of graphic hack the map reading tendencies of our brain or prime our natural pattern seeking.

4. Idea generation

The lower left quadrant, Berinato calls, “Idea generation.” These are the kinds of figures scientists scribble up on white boards when we are thinking through experiments. Rarely do these graphics make it out into the world, rather they help us think through our own ideas. However, sometimes conceptual, exploratory graphics are useful for thinking through hypotheses. For example, I included the graphic below in my dissertation prospectus as a way to think through how geneflow patterns might look in different populations.

Understanding why and how it makes sense to use graphics can save you loads of time, keep you from making spurious plots, and may even lead you to a new discovery. Fortunately, professional plotting tools like (R and GIMP2) are freely available. So get out there and start making something beautiful and useful!

I’ve replicated all of the slides and script below, but first, here are the bullets if you’re short on time.

To make better presentations, try:

• Reorient your presentation to optimize for short attention spans. This means stating your conclusion first, then presenting your data, then connecting it to the broader topic.
• Minimize distractions. Avoid designs and presentation strategies that will distract from your main topic.
• Bad storytelling can be a distraction from even the best story, so please, practice your #$*&ing presentation!
• Use figures to tell stories, not to show data. Reduce data stories to minimum story units and only tell one story unit at a time using layers, starting with the axes.
• I also include a number of simple PowerPoint hacks that will help you avoid common errors that lead to wonky slides.

Here’s my full presentation:

What is the point of academic presentations? The obvious answer is “to transmit information about our science.” Realistically, most of us are also motivated by little more than adding a new line to our CV. An underappreciated purpose of presentations is as a vehicle for networking. Presentations are like calling cards and signal to your audience if your work is interesting. It also give audience members a brief window into you as a scientist.

We need to give good presentations because, first, the whole damn point is to transmit our information. Bad presentation are a barrier to that transmission. Second, we need to give good presentations because presentations are like our tinder profiles to collaborators. A bad presentation indicates that you do not care enough about your work or your peers in the audience to bother making a tolerable presentation.

Some might question the wisdom of signing up to give a presentation about not giving bad presentations. But you should be able to guess from the title of my talk that I think the bar is exceptionally low. More often than not, academics give pretty terrible presentations. But there are a few simple things you can do to give really great presentations.

It basically boils down to just two key elements:

Optimize for attention

Minimize distractions

Let me show you what I mean by Optimizing for Attention.

Almost every science presentation I have ever seen is organized the same way that we organize papers.

We start with some big picture background questions, then we talk about our specific question, then we explain our system, then we detail our methods, then we talk through our results, and if we have enough time at the end, we finally get to our conclusion, which is the main punchline of the talk. That is the chocolate at the center of the tootsie-pop.

The problem with this model is that our attention span diminishes over time. So, we end up wasting all of the optimal attention window laying out the least important information, and we wait until half the audience has zoned out before delivering the punchline.

Lindquist & McLean (2011) showed how attention diminishes over time by surveying folks during 45-minute lectures. They sounded a signal at intervals during presentations and then had respondents indicate if they were thinking about something unrelated to the lecture, called Thoughts or Images Unrelated to Tasks (= TUITs (basically, day dreaming)). The frequency of daydreaming increased over time until only about half of the audience was focused at any given time towards the end of the presentation.

To optimize for attention spans, one option is to flip your presentation to match the audience’s daydreaming frequency. Give the punchline first. Rather than starting by explaining why your question matters, start with your conclusion and then explain how it is relevant.

Even though this might seems strange, it is actually how we read papers. Rarely do we ever read papers linearly. Most people skip to the last line of the abstract to get the punchline, then maybe read the abstract or figure captions, then the conclusions. Maybe you read the methods or the text of the results section next, but the last thing you read is the background or introduction.

Even if you arrange your presentation to optimize for folks attention span early, we still don’t want to lose most of our audience along the way. We’d like to keep folks’s attention as much as possible.

We humans are a very distractible animals. We spend 47% of our day distracted by other things. And we are most distracted at the times we are trying to concentrate the most!

This makes maintaining attention even more difficult in learning environments, because we will naturally seek out distraction.

For example, kids in decorated classrooms performed 25 – 35% poorer on tests because of the easy distraction from the environment. Interestingly, even without environmental distraction, kids simply switched to being distracted by each other.

In the case of classrooms, there are likely many other benefits of busy classrooms that outweigh the distractions they cause. But in the short span of a presentation intended for adults, we should be aiming for the starkest, least distracting design.

Just to drive home my point that we humans are overly distractible, look at the next slide and time how long it take you to find the let “O”.

Now try it again with the next column.

When researchers repeat these kinds of tests over and again, they find that just adding a simple distraction, like the cartoon, substantially increases your processing time because your mind splits your attention to processing the distracting image.

So, given what I just told you about how easy it is to distract a human, what is wrong with this fictitious but typical academic style slide?

The problem with this type of slide is that there is too much going on. Even the most aggressively wielded laser pointer will not be able to focus the audience’s attention to one element at a time without distraction.

It is also important to remember that even the best story can be ruined by a bad storyteller. Poorly practiced and desultory presentations can be a huge distraction. So, in addition to minimizing visual distractions, remember to…

In the next section, I will go over some pointers and presentation hacks to avoid slides like this one.

Using figures

Academics love to put up complex figures with loads of distracting and unnecessary information on slides. Which kind of makes sense, really. After all, you spent hours collecting each little point of data, so to you, every data point is important. But that’s often not the case. Our goal with figures is to tell a story. It’s not to show off how much work we did, or how complicated our designs are. We want to distill our figures down to the smallest possible story units.

Take a look at this figure from an “experiment” I conducted. For this fictitious experiment, I was interested in how beard color and length correlates with crossword completion speeds. I went to 16 towns. In each town I gave 100 bearded folks a crossword and recorded how many cells they completed per minute and measure their beard length. In every town, half the folks had red beards and half had brown. I noted if they had proper beards or goatees (type).

Think about the minimum units of the story here. What are some ways you might be able to make this figure simpler and less distracting?

Here is my revision. All of those original cells told basically the same story. And this image answers my primary question: “How does beard length and/or color impact crossword speed?” There is no need for the other information in the figure.

Now, here is how I would present this slide. I do it in layers, starting byexplaining the axis and what we should expect to see.

Then we layer on one bit of information.

Then the next bit of information.

But there are some times when distilling down all of the information into one figure is difficult or doesn’t answer our question.

In this next fictitious experiment, I wanted to know if dragon size correlated with the number of villagers eaten. I recorded three different species of dragons in three different years (years is in time from present).

In this case, there is no obvious story–the relationship changes between years and with different dragon species. Also, our sample size are very different, so we need to have a way of relating that we are more confident in some relationships than others.

Here is how I decided to tell this data story.

First, I start with the blank axis and explain what they mean.

Then I add the first year of information.

Then, I add the second year of information. But I still want folks to see the prior information, so I use selective highlighting to focus their attention. We can see that in all cases, more villagers were consumed. And the rate of growth increased.

Now we add the third element. And we can tell the whole story. Every year, dragons eat more villagers, but species differ in the level of increase. Also, the relationship changes over time with respect to body size for different species of dragon.

To recap the whole story, I might show just the trends lines and confidence bands.

It can be really hard to strip down figures, especially if you are afraid that someone might question your data. When I make presentations, I always keep all of my original figures in extra slides at the end of my presentation, after the conclusion slide. I never show those slides in the presentation, but if someone asks a specific question about the data, I can easily flip to the more informative figure.

Other trick and tips

You can use the same kind of selective color to focus attention with text, too.

I also want to touch on some problems that I see too often and tell you how to avoid them.

Have you ever seen a presentation that looked fine on your computer, but came out looking like this at your conference talk?

This happens when you use a font on your computer to make a presentation that is not installed on the computer that you use to display the presentation. The computer defaults to what it thinks the next closest font should be, and it is always wrong.

The easiest solution is to simply export your slides as JPEG image files and put each one back onto a slide. Essentially, your slide is now a picture of your original slide. That way, it will be displayed exactly as you see it on your computer wherever you display it.

One quick word while we are talking about fonts. Please try to pick simple fonts (like those on the left). You should only use the fonts on the right if you are creating a title page for your 5th grade history report.

Have seen presentations with figures that look like this?

This happens when you enlarge an image in bitmap format. Essentially you are trying to display more pixel than in the original image. The computer interpolates new pixels by averaging adjacent pixels, but it is fuzzy and pixelated. The solution here is to either use vector based images or bitmaps that are as large or larger than the display size. If you are using an image from a paper, try to download the largest image size. If you can only take a screen shot, be sure to blow it up as large as possible before capturing the screen.

And in conclusion…

Please don’t do this at the end of your presentation. No one needs to know every organization that has ever given you money. And it is great to thank people, but if everyone is special, no one is special. Instead of making your final slide into a Guess Who board, consider alternative options to show gratitude. For example, if an undergrad was integral to an experiment, pop up their photo on the results slide and thanks them then. If an advisor was especially helpful, they’ll appreciate a handwritten thank you note more than a pixelated mug shot at the end of your presentation.

Keep your conclusion slide simple. Use the final slide to give your audience ways to learn more. For instance, I try to make a blog post about my presentation that folks can use to find out more information, check out my references, or see my original figures.

Good luck and happy presenting!

The next step is to take the points below (along with any comments that you might have!) and work them into a manuscript formally calling for an Ethic of Road Ecology. Please let me know if you have thoughts, would like to contribute a case-study, or would like to be involved in any other way with this project.

Do we need an Ethic of Road Ecology?*

As a field, road ecology is built on the fulcrum of balance between contending goals: acknowledgement of human need for development and concern for the environment. At its heart, the field is laden with an ethical quagmire that goes almost entirely unaddressed.

To illustrate this point, I’d like to have you consider the Trolley Problem.

The trolley problem goes something like this, imagine you are in the control station for a train approaching a fork. On the main track, you see four people asleep on the track. You cannot alert the folks on the track, but you have the lever to switch the train onto a side track. Unfortunately, there is a single person on that track.

What would you do? Would you have two options, take no action and watch the train run over four people on the main track. Or, actively pull the lever to switch the track, killing the single person.

This thought process is useful because the binary here forces us to grapple with moral valuations—are multiple lives more valuable than one, and regardless of the choice, do we feel morally responsible for action versus inaction.

Now let me change some of the parameters here to show how transportation engineers are presented with real life trolley problems every day.

Now imagine that, rather another person on the side track, switching the train onto the side track would just take the train on a more circuitous route and extend the trip.

I think everyone would argue to flip the switch. In fact, it would probably be hard to imagine an amount of delay to the train passengers that would justify loss of life. And yet, this is exactly the dilemma we face when designating speed limits.

A universal speed limit of 5 mph would prevent almost all traffic-related mortality, but we probably also all agree that that’s an outlandish solution. After all, we want not only “safe” transportation, but also “efficient” transportation, as codified in the mission statements of most DOTs.

What if, on the side track, is a pile of money that would be completely obliterated by the train? How many dollars would for us to think it ethical not to flip the switch?

Again, hopefully you can see the analogy: in a world with finite budgets, allocations for transportation safety forces us to take out a moral calculator and determine the amount of money in the bag?

Dilemmas like these illuminate the ethical foundations of decision that we otherwise wouldn’t think had anything to do with ethics. It also forces us to uncover our internal moral calculators and really think about how we plug in the moral values behind the valence we reflexively feel toward the outcomes.

Thus far, these examples have involved humans which implies a moral valence. They force us to contrast our deontological or duty-based ethics against utilitarian ethics.

We can take humans out of the equation and imagine dilemmas that force us into a valuation of aesthetics, too. And we can imagine analogies not only to existing transportation management, but to the dilemmas inherent in planning new projects. For instance:

What if the alternative track forever routed trains through the middle of Notre Dame?

What if it routed the train through your neighborhood or right through your front lawn?

What if we replace the human on the track with a herd of deer?

How big does the dollar sign have to be to decide not to switch the tracks? Or conversely, how many deer?

What if instead of deer, this is the last wild rhino?

What if it is the last swam of an endangered mosquito?

These cases invoke an environmental ethic. This also bring this preamble to the topic of this conference, road ecology.

As practitioners of transportation ecology, we are all involved in this ethical project. Folks on the science side help us to define the variables: “how big is the dollar sign, how many deer?”, while folks on the engineering and management side help us define the alternative tracks. Ultimately, as citizens, we all collectively determine which parameter are important to consider and their relative value.

In reality, the field and practice of road ecology is not simple dichotomies. It looks more like this. Where the ethical decision involves complex valuation of trade-offs between combinations of moral, ethical, and aesthetic impacts.

If you had a hard time naming exact values in those first couple iterations of the trolley problem, then you should recognize just how difficult it would be to put finite ranks to all these alternative routes.

Determining the most ethical path requires lots of science and lots of project evaluation. Doing it well can take a LONG time and lots of effort. But all that effort and time can be fraught if those valuations and the process are opaque and unexamined.

A good example is the reconstruction of US93 through the Flathead Indian Reservation. That project was stalled for 15 years because the first iteration of the project plan did not consider the broad scope of ethical factors that were important to the local residents and Confederated Salish and Kootenai Tribes.

We propose that our field is in urgent need of adopting an ethic of road ecology.

Why it matters:

• Practitioners are asked to grapple with difficult decision-making tasks without the proper skills and tools. To date, the field has focused almost entirely on the pragmatic tools to solve the symptoms of roads, but none of the tools with which to diagnose the problems.
• Even worse, when decisions are made and projects developed, there is no established way for vetting the process or outcomes (positive or negative) in a way that contributes to and accumulates knowledge within the field.
  1. Best-practices and guidelines are insufficient. We write best-practices and guidelines, but we don’t have a mechanism to critically compare and contribute to them as a field. Nor a mechanism to ensure that those documents are addressing all of the potential problems.
• As a field, we are really good at answering pragmatic questions about the interface of transportation and ecology. We are really bad at evaluating which questions to ask in the first place.
• There is a danger that road ecology could be used as a justification for greater impact.
• There is a strong argument that as parties to the development of roads, we are moral responsible for fully recognizing and addressing their impacts.
• We can only expect more roads and infrastructure in the future, so these ostensibly minor cracks at the foundation of the field will only grow into larger faults if unaddressed.

The solution:

That we need an ethic of road ecology is easy to say in the abstract, but what would it look like?

There are good examples of how entire fields grapple with the ethical issues inherent in their practice.

For instance, the medical field has a robust sub-field of medical ethics that dates back to the Greeks.

Biologists have the field of bioethics and animal welfare ethics.

Even within ecology, there is a wealth of literature in environmental ethics that deals specifically with the ethics of ecological restoration that dates back to Aldo Leopold and his contemporaries, but flourished in the 80s and 90s.

To date, the ethics of ecological restoration have been considered in numerous articles and books. Even the primary organization of the field, the Society for Ecological Restoration has adopted a Code of Ethics and publishes a full text: Ecological Restoration: Principles, Values, and Structure of an Emerging Profession that deals explicitly with the ethics and values embedded in the practice.

Given those models, the ethical conversation in ecological restoration could be the best analog upon which to build an Ethic of Road Ecology.

But we argue that Road Ecology, as a field, is mature enough and the issues it deals with are distinct enough to warrant its own specific discussion of ethics.

We already have great case studies (good and bad) within the field to work from:

In the U.S.:

• The Paris-Lexington Road
• S. Highway 93
• I-90 Snoqualmie pass
• US Hwy 260 Arizona Tonto NL
• I-75 Alligator Alley FL
• And there are multiple international case-studies in the Handbook of Road Ecology

Specifically we propose:

• Drafting a Code of Ethic for Road Ecology similar to SER.
• Encourage conversations and publications about the ethics of road projects by soliciting presentations, panels, and/or symposia on these topics at conferences.
• Create a platform for discussing case-studies, either in article form through journals or dedicated sessions at conferences.
• Develop guidance in ethical decision-making for practitioners involved in planning transportation projects and include this in best-practice guidelines and future manual and texts about road ecology.

The utility of explicitly addressing road ecology with an ethical framework can extend beyond the practical aspect of a mechanism for evaluating the best project alternatives.

• Most importantly, an open discussion about the values and priorities as a field will bring to light the internal contention we all feel when forced to weigh outcomes, as evidenced by any reflexive confusion you may have felt about the trolley problems I presented.
• Being upfront about values and trade-offs prevents the field from inadvertently becoming a tool of greenwashing to justify even greater degradation.
• Explicating value structures will force us to define the boundary conditions for ethical projects and provide ground for determining, when necessary, where no alternative can be justified.
• It will push the field forward. In much the same way that Forman’s concept of the “road-effect zone” expanded our conceptual jurisdiction of ecological impacts of roads, an ethical framing will force us to further reconsider the extent our responsibility bleeds out from roads. For instance, even if we can fully mitigate a proximate section of road, are we responsible to also mitigate the distal effect if the road section opens up a pristine watershed to development?
• Transportation infrastructure is probably the main driver of ecological change across the globe, and there is no indication it will abate. Unfortunately, we cannot stand still on a moving train (multiple puns intended). The alternative to our proposal of explication ethics is that we either ignore them or make opaque decisions without critical evaluation. This is too often the case in transportation projects. But ignoring the foundation of an action, or lack of action, does not absolve us of the responsibility. Whether we flip the switch or let the train run its course, we are still culpable for the outcome. There is no absolution in inaction.

In the near-term we would like to formally propose and ethic of road ecology in a journal article. If you would be interested in helping to develop that manuscript and in particular if you have case-studies you’d like to contribute, please, let’s talk.

* This presentation was developed in collaboration with:

Dr. Marcel Huijser, Western Transportation Institute at Montana State University

Dr. Daniel Spencer, University of Montana Department of Environmental Studies

Dr. Bethanie Walder, Society for Ecological Restoration

Here’s my talk from the conference, “Evolution of Intrinsic Rates: Can adaptation counteract environmental change?“:

I was only able to stay for the day of my talk and missed the award ceremony on the second day. So it was quite the surprise when I visited the Conference’s website to register for this year and saw that I had won an award for the best speed talk!

Check out the video of my talk below:

Or follow this link to the video.