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.

The study:

One of the most consistent findings arising from 20 years of study in our lab is that wood frogs seem to adapt to life in cold, dark ponds. In general, cold-blooded animals like reptiles and amphibians are not suited for the cold and function much better in warmer conditions. So, wood frogs that live in colder ponds should have a harder time competing against their neighbors in warmer ponds.

In response, cold-pond wood frogs seem to have developed adaptations that level the playing field. In separate experiments, we’ve found that wood frog tadpoles in cold-ponds tend to seek out warmer water (like in sunflecks) and have lower tolerance to extremely warm temperatures. Most importantly, they can mature faster as eggs and larvae.

But I’ve always struggled with a lingering question: if cold pond frogs have adapted these beneficial adaptation to compete with warm-pond frogs, what is keeping those genes out of the warm-ponds? Shouldn’t cold-pond genes in a warm pond mean double the benefits? One would expect the extrinsic environmental influence and the intrinsically elevated growth rates to produce super tadpoles that metamorphose and leave the ponds long before all the others.

Kaija, who was an undergrad in the lab at the time, decide to tackle that question for her senior thesis.

We hypothesized that there might be a cost to developing too quickly. Studies in fish suggested that the trade-off could be between development and performance. The idea is that, like building Ikea furniture, if you build the tissue of a tadpole too quickly, the price is loss of performance.

So we collected eggs from 10 frog ponds that spanned the gradient from sunny and warm to dark and cold. We split clutches across two incubators that we set to bracket the warmest and coldest of the ponds.

Then we played parents to 400 tadpoles, feeding and changing water in 400 jars two to three times a week.

We reared the tadpoles to an appropriate age (Gosner stage 35ish). Those in the warm incubator developed about 68% faster than those in the cold incubator. In addition to our lab-reared tadpoles, we also captured tadpoles from the same ponds in the wild as a comparison. Development rates in the lab perfectly bounded those in the wild.

Once they reached an appropriate size, we put them to the test. We simulated a predator attack by a dragon fly naiad by poking them in the tale. Dragonfly naiads are fast, fierce, tadpole-eating machines and a tadpole’s fast-twitch flight response is a good indicator of their chance of evading their insect hunters. It’s a measure of performance that directly relates to a tadpole’s fitness.

Above the test arenas, we positioned highspeed cameras to capture the tadpoles’ burst responses. We recorded 1245 trials, to be exact—way more than we ever wanted to track by hand. Fortunately, Kaija is a wiz at coding; and with a bit of help, she was able to write a Matlab script that could identify the centroid of a tadpole and record its position 60 times per second.

We measured the tadpoles’ speed during the first half second of their burst response and looked for an association with their developmental rates. One complicating factor is that a tadpole’s fin and body shape can influence burst speeds. So, a weak tadpole with a giant fin might have a similar burst speed to a super fit tadpole with a small fin. To account for this, we took photos of each tadpole and ran a separate analysis mapping their morphometry and included body shape into our models.

As we had hypothesized, tadpoles reared at warmer temperatures show much slower burst speed than their genetic half-sibling reared in the cold incubator. We even saw a similar, but weaker effect for the tadpoles that were allowed to develop in their natal ponds. It seems that developing too fast reduces performance.

Thus, it certainly seems that the counter-gradient pattern we see of faster development in cold-pond populations, but not in warm-pond populations, is at least partially driven by the trade-off between development rate and performance.

In fact, it may even be the case that we’ve been viewing the pattern backwards all along. Perhaps instead we should consider if warm-pond populations have developed adaptively slower development rates to avoid the performance cost. This especially makes sense given the range of wood frogs. Our populations are at the warm, southern end of the range. Maybe this tradeoff is also a factor constraining wood frogs to the cold north of the continent?

If warm weather and faster development are a real liability for wood frogs, it is only going to get worse in the future. We know from another recent study that our ponds have been warming quickly, especially during the late spring and early summer months. But climate change is also causing snow to fall later in the winter forcing frogs to breed later. The net result is that wood frogs may be forced to develop fast intrinsic developmental rates in response to a contracting developmental window, while at the same time, extrinsic forces drive development even faster. That’s a double whammy in the trade-off with performance. And might lead to too many “Ikea furniture mistakes” at the cellular level.

As a separate part of this study, we also measured metabolic rates in out tadpoles in hopes of understanding the relationship between developmental rates, performance, and cellular respiration. I’m in the process of analyzing those data, so stay tuned for more!

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

Rats have traveled around the globe alongside humans. Where we make our homes, they are happy to make their own, too. Unfortunately, they can make terrible neighbors, especially when they become vectors for diseases.

The rats that live in the slums of Salvador, are to blame for the widespread of emergence of leptospirosis infections that poses a serious health risk. In response, the municipality planned a multi-year rat-eradication project.

The bad news is that it is nearly impossible to fully eradicate rats from large territories. After just a short time, the populations tend to rebound, either because some rats were missed and then repopulate from within or because new rats migrate from outside and colonize the area. Understanding how rats repopulate after eradication efforts is important for deciding how best to proceed with future management strategies.

To test which of these scenarios was at play in Salvador, my collaborators trapped rats before, during, and after the eradication campaign in three contiguous geographic regions within the slums. We looked at the genetic relatedness of the populations across the three time-points. If the rebound populations were more similar to each other post-eradication, it would suggest that the populations were recolonized from source populations outside. However, if the regions became more genetically distinct post-eradication, it would suggest that the rebound populations were seeded from the few local genetic lineages that persisted.

We found that the populations showed distinct genetic differences immediately after the eradication effort, suggesting that remnant rats from the original populations had repopulated from within. Those difference persisted after 7 months.

In addition, we looked at the genetic signatures of population expansion and contraction. We found that post-eradication populations had pronounced reductions in genetic diversity. As animals were removed from the population during eradication efforts, it created a bottleneck. Since the new populations arose from the few remaining rats, only those few genetic variants left over persisted.

If enough genetic diversity is lost, and is not replaced by migrant mice from outside the original population, it might eventually lead to inbreeding and reduced fitness. If that is the case, it means that creating serial bottlenecks in the population through eradication efforts could be an effective way to weaken the rat populations to manageable levels, even if the overall population number rebounds in the short term.

I think my collaborator Dr. Jonathan Richardson (@JRichardson_44) did a nice job of succinctly outlining our findings in this Twitter thread:

I'm very excited about our new paper out today on the significant genetic impacts that accompany an urban eradication campaign of rats in 3 areas of Salvador, Brazil, w/ @georgisil_ & many Twitterless colleagues. The TL,DR version is in short thread below:https://t.co/WLqmQ1UJkD

— Jonathan Richardson (@JRichardson_44) June 6, 2019

It might be surprising, but tree canopies can have major impacts on aquatic amphibians. The greater the canopy cover, the less light will reach a ponds surface. Wood frog tadpoles are adapted for rapid development and every small increase in water temp from every small stream of sunlight can make or break a tadpole’s survival. In addition to temperature, the amount of light incident on a pond dictates the amount of algae growth, which in turn dictates the concentration of oxygen in the water. Also, the amount of algae can dictate how much food competition exists between tadpoles, and how bold tadpoles must be to seek out food. In a pond full of predators, being bold can have dire consequences.

More leaves in the canopy also means more evapotranspiration, which in turn means greater water demand from tree roots. As tree suck water out of the ground, the water levels in the pool drops and eventually dries out completely. Amphibians are in a race against time to metamorph before that happens.

So, as you can see, tadpoles and trees have a surprisingly close relationship. All of these compounding factors allow us to tell a lot about wood frog evolution, just by looking up.

The darwin is a great unit if you are interested in long-term macroevolutionary change, but sometimes it falls short for certain questions. For instance, what if we wanted to compare the evolutionary rates of a tree and its insect pest? One limitation of the darwin that Haldane noted was that it doesn’t account for generation times. Over the order of millions of years, it probably is a wash, but if you are interested in short time windows, say 1000 years, generation times matter, especially when comparing a tree with 100 year generation time and an insect that reproduces annually. For this purpose, Philip Gingerich proposed a unit he dubbed the haldane.² The haldane is similar to the darwin, but the rather than directly comparing mean trait values it compares trait values scaled to their standard deviation and rather than measuring time in millions of years, it measures time in generations. Here’s how it looks:

The haldane is good unit for change over time, but what if we are also interested in divergence over space? For instance, what if we are interested in comparing rates of evolutionary change in populations of fish in a river channel? We would expect there to be more divergence in population at the headwaters and the outlet than between populations just a few stream-miles apart, so how can we account for that? Richardson et al. proposed a unit for just this question in 2014.³ They suggested that there is a radius in which we would expect gene flow to disallow divergence. This is the dispersal kernel, depicted in their paper. Within that radius we would be surprised if trait values differed between populations, but outside we might be less surprised. Therefore, they scaled their unit to a distance ratio with that radius. To keep with tradition, they named their unit the wright after Sewall Wright, one of the fathers of population genetics. In their formulation, the difference in trait means is compared as in the darwin, but it is scaled to the pooled standard deviation and the distance ratio.

And this leads me to my current adventure in evolutionary analysis: how to best visualize the pairwise divergence of multiple populations with overlapping dispersal kernels? In my case, I have phenotypic traits for 15 population. I would like to visualize the pair-wise difference in traits with respect to their geographic distance. To further complicate things, I’m not entirely sure what distance to assign for a standard dispersal kernel.

My strongest idea so far has been to display a pair-wise matrix with wright values in the lower triangle and geographic distance in the upper triangle. I ran an ANOVA on trait means by location and used the multiple Tukey HSD post hoc comparison p-values to assign a shading to significant divergence in the lower triangle. In the upper triangle, red shaded values indicate distances less than a 1000m dispersal kernels. The outlined squares in the lower triangle indicate significant divergence within a dispersal kernel, or microgeographic divergence as defined by Richardson et al. 2014. Since I’m not sure what an average dispersal distance might be, I ran the same analysis for 500m, 1000m, and 2500m (only the 1000m analysis is shown). To help illustrate the matrix, I’ve include a nearest neighbor joining spatial plot for each dispersal kernel distance (500m, 1000m, and 2500m).

There is a lot of information digested into this one plot. So I’d be happy to hear any thought on how I could improve it.

¹ Haldane, J.B.S. (1949). Suggestions as to quantitative measurement of rates of evolution. Evolution 3:51–56.

² Gingerich, P.D. (1993). Quantification and comparison of evolutionary rates. American Journal of Science. 293-A, 453-478.

³ Richardson, J.L. et al. (2014). Microgeographic adaptation and the spatial scale of evolution. Trends in Ecology and Evolution. 29(3), 165-176.