+
+
+
+
+
+
+
+Advanced Functionality and Writing Extensions
+ + + Source:vignettes/advanced_functionality.Rmd
+ advanced_functionality.RmdTBA
+- Advanced Functionality and Writing Extensions +
- +
- Stratigraphic Paleobiology for Event Data
- Stratigraphic Paleobiology for Phenotypic Evolution
-
diff --git a/articles/phenotypic_evolution.html b/articles/phenotypic_evolution.html
index 16637b9..1bfc1b8 100644
--- a/articles/phenotypic_evolution.html
+++ b/articles/phenotypic_evolution.html
@@ -34,7 +34,8 @@
Stratigraphic Paleobiology for Phenotypic Evolution
-Introduction
Here we describe the modeling pipeline for the stratigraphic -paleobiology of trait evolution.
+paleobiology of phenotypic evolution. First, let’s load all required +packages: +-Time domain +
Modes of evolution in the time domain
++Implemented modes of evolution +
The
+simulating different modes of evolution in the time domain:StratPalpackage provides three functions for -simulating trait evolution in the time domain:stasissimulates evolutionary stasis as independent, normally distributed random variables with meanmeanand @@ -89,69 +94,134 @@Time domain +
Visualization +
+You can visualize the different modes of evolution using the +following pipeline:
-
seq(0, 1, by = 0.01) |> # times of simulation in myr. simulate over 1 Myr years with 10 kyr resolution - random_walk(sigma = 1, mu = 3) |> # simulate random walk with incresing trait values - plot(type = "l", # plot results +seq(0, 1, by = 0.01) |> # times of simulation in myr. simulate over 1 Myr years with 10 kyr resolution + random_walk(sigma = 1, mu = 3) |> # simulate random walk with increasing trait values + plot(type = "l", # plot results xlab = "Time [Myr]", ylab = "Trait value")
+
Task: Modify the pipeline to get an intuition for +the different modes of evolution. What are the effects of their +parameters, and what is their biological meaning?
+
+Stratigraphic domain
-We are interested in the stratigraphic expression of phenotypic -evolution, i.e. how is trait evolution within a lineage preserved at a -specific location in the stratigraphic record.
-For this, we first define an age-depth model:
+We are interested in how phenotypic evolution is preserved in the +stratigraphic record, i.e. how is trait evolution within a lineage +preserved at a specific location in the stratigraphic record. Here we +develop the modeling pipelines to answer this question.
+++Age-depth models +
+As an example, we compare the the preservation of trait evolution 2 +km and 12 km offshore in the carbonate platform in scenarion A (see +
?scenarioA). For this, first define the age-depth +models--
# define ADM 4 km from shore -dist = scenarioA$dist_from_shore[2] -adm = tp_to_adm(t = scenarioA$t_myr, - h = scenarioA$h_m[,dist], +# define ADM 2 km from shore +adm_2km = tp_to_adm(t = scenarioA$t_myr, + h = scenarioA$h_m[,"2km"], + T_unit = "Myr", + L_unit = "m") + +adm_12km = tp_to_adm(t = scenarioA$t_myr, + h = scenarioA$h_m[,"12km"], T_unit = "Myr", L_unit = "m") + # plot age-depth model -plot(adm, +plot(adm_2km, + lwd_acc = 2, # plot thicker lines for accumulative intervals (lwd = line width) + lty_destr = 0) # don't plot destructive intervals (lty = line type) +T_axis_lab() # add time axis label +L_axis_lab() # add length axis label +plot(adm_12km, lwd_acc = 2, # plot thicker lines for accumulative intervals (lwd = line width) lty_destr = 0) # don't plot destructive intervals (lty = line type) T_axis_lab() # add time axis label L_axis_lab() # add length axis label
-
Now we can use the age-depth model to transform the simulated trait -values from the time domain into the depth domain using +
+
++Stratigraphic expression of phenotypic evolution +
+We use the defined age-depth models to transform the simulated trait +values from the time domain into the depth domain via
time_to_strat:+-
# sample every 10 kyr over the interval covered by the adm -# simulate random walk -# transform data from time to strat domain -seq(from = min_time(adm), to = max_time(adm), by = 0.01) |> - random_walk(sigma = 1, mu = 3) |> # simulate random walk - time_to_strat(adm, destructive = FALSE) |> - plot(type = "l", ++seq(from = min_time(adm_2km), to = max_time(adm_2km), by = 0.01) |> # sample every 10 kyr over the interval covered by the adm + random_walk(sigma = 1, mu = 3) |> # simulate random walk + time_to_strat(adm_2km, destructive = FALSE) |> # transform data from time to strat domain + plot(type = "l", # plot + orientation = "lr", + xlab = "Stratigraphic height [m]", + ylab = "Trait value")
+This is what a biased random walk in the time domain would look like +if it was observed 2 km offshore in the simulated carbonate platform. +The large jumps in traits correspond to long hiatuses caused by +prolonged drops in relative sea level. Compare this figure to the random +walk in the time domain (without stratigraphic distortions.)
+12 km offshore, the preservation is very different:
++
seq(from = min_time(adm_12km), to = max_time(adm_12km), by = 0.01) |> # sample every 10 kyr over the interval covered by the adm + random_walk(sigma = 1, mu = 3) |> # simulate random walk + time_to_strat(adm_12km, destructive = FALSE) |> # transform data from time to strat domain + plot(type = "l", # plot results orientation = "lr", xlab = "Stratigraphic height [m]", ylab = "Trait value")
-
Here we can already see the large jumps in trait values over the -gaps, because we are missing a lot of time.
-This is already a good start, but there are some small issues: -Because we simulate the lineage every 10 kyr, the lineage is sampled -irregular in the stratigraphic domain. For example, we get multiple -conflicting trait values at height with gaps longer than 10 kyr. To get -a more realistic result, we need to prescribe a sampling strategy in the -stratigraphic domain. We do this in the following steps:
+You can also see two jumps, but the first is at a different location +compared to the section 2 km offshore. Looking at the age-depth models, +we can see why this is: 2 km offshore, the jumps are caused by prolonged +hiatuses that are associated with the massive drops in sea level around +0.5 Myr and 1.5 Myr. 12 km offshore, the age-depth model shows fewer +gaps, but prolonged intervals with low sedimentation rates at the +beginning of the simulation and at around 1.5 Myr. This leads to +stratigraphically condensed intervals, at which phenotypic evolution +appears to be accelerated. You can see that not all artefactual jumps in +traits observable in the stratigraphic record are caused by gaps.
+Task: How does this effect vary between different +modes of evolution (with different parameter choices), and at different +locations in the platform (e.g., along an onshore-offshore gradient)? +Can you make any general statements about where you see the strongest +effects?
+++Prescribing a sampling strategy +
+The above plots already give us a good idea of how stratigraphic +effects can change our interpretation of trait evolution.
+However, there is a small imperfection. Because we simulate the +lineage every 10 kyr, it is sampled irregularly in the stratigraphic +domain: If sedimentation rates are high, samples can be multiple meters +apart, but if sedimentation rates are low, they are only a few +centimeters apart.
+To fix this, we want to prescribe where in the stratigraphic domain +samples are taken. This allows us to examine the effect that different +sampling strategies have on our perception of trait evolution. We do +this in the following steps:
-
-
- Determine sampling locations +
- Determine sampling locations in the stratigraphic domain
- Calculate what times correspond to said sampling locations via
strat_to_time
@@ -163,24 +233,42 @@
Stratigraphic domain
+
dist_between_samples_m = 2 sampling_loc_m = seq(from = 0.5 * dist_between_samples_m, - to = max_height(adm), + to = max_height(adm_2km), by = dist_between_samples_m) -sampling_loc_m |> # sampling locations - strat_to_time(adm) |> # determine times where lineage is sampled - random_walk(sigma = 1, mu = 3) |> # simulate trait values at these times - time_to_strat(adm, destructive = FALSE) |> # transform trait values to stratigraphic domain - plot(orientation = "lr", - type = "l", # plot fossil time series +sampling_loc_m |> # sampling locations + strat_to_time(adm_2km) |> # determine times where lineage is sampled + random_walk(sigma = 1, mu = 3) |> # simulate trait values at these times + time_to_strat(adm_2km, destructive = FALSE) |> # transform trait values to stratigraphic domain + plot(orientation = "lr", # plot stratigrahic data + type = "l", ylab = "Trait Value", xlab = "Stratigraphic Height [m]")
There we have it! This is what the lineage would look like if it -would be sampled every 2 meters in a sections 2 km offshore. You nicely -see the large jumps over the prolonged hiatuses.
+would be sampled every 2 meters in a sections 2 km offshore. +Task: How does prescribing a sampling strategy +change the interpretations of your last task? Can you draw any +conclusions about which sampling strategy is best suited for a given +environment or mode of evolution?
++diff --git a/articles/phenotypic_evolution_files/figure-html/figures-side-1.png b/articles/phenotypic_evolution_files/figure-html/figures-side-1.png new file mode 100644 index 0000000000000000000000000000000000000000..6602ba47768b4293c3eb5c8637d4ebe15ad7c3c6 GIT binary patch literal 22074 zcmeFZc~q0v`!5Further reading +
+Go to
+++
vignette("phenotypic_evolution")for details on how to model stratigraphic paleobiology of phenotypic +evolution, or explore the vignette online on the package webpage (mindthegap-erc.github.io/StratPal/articles/phenotypic_evolution).
+See also
+++
vignette("advanced_functionality")for details on how to expand on the modeling pipelines described +here, or explore the vignette online on the package webpage (mindthegap-erc.github.io/StratPal/articles/advanced_functionality).
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