Blog Archives

Reading environmental history from peat (Pymatuning Wetlands 2015, Day 12)

Today the Pymatuning wetlands spent the entire day in the lab. Our first day without any fieldwork since the course began.  However, we made up for it by doing a bit of time travel…

Examining plant microfossils from a peat core collected from Titus Bog.

Examining plant microfossils from a peat core collected from Titus Bog.

We examined the core we collected from Titus Bog yesterday.  We subsampled the sediment and peat, sieved the samples to isolate plant macrofossils (i.e., seeds, leaves, needles, etc.), and identified and tallied the microfossils to determine how the vegetation of the wetland has changed over the past 8000 or 9000 years.  The students determined that the site was occupied by a shallow lake prior to the establishment of the modern peatland, with submerged and floating leaved aquatic plants like Najas (water nymph), Nuphar (spatterdock), and Nymphaea (water lily) growing in the deeper portions of the littoral zone. Emergents like Cladium (sawgrass), Rhynchospora (beaked sedge), and other sedges likely occupied the lake margin along with small amounts of Sphagnum moss. The area abruptly became a floating peatland about 350 years ago, when Sphagnum became dominant.  The upland vegetation around the site contained Tsuga canadensis (hemlock), Pinus strobus (white pine), and Betula alleghaniensis (yellow birch) for much of the record. Most of the species in the paleoecological record have been observed at the wetlands we have visited during the past two weeks of the course; in fact, quite a few are the “must-know” list.

Summary macrofossil diagram from Titus Bog, PA.  Numbers per 10cm3 are plotted against depth in the core. Ages, in years before present, were estimated from Ireland and Booth (2011). The microfossil record was put together in one afternoon by seven students, with each student analyzing about 10 samples.

Summary macrofossil diagram from Titus Bog, PA. Numbers per 10cm3 are plotted against depth (cm) in the core. Ages, in years before present, were estimated from Ireland and Booth (2011). The microfossil record was put together in one afternoon by seven students, with each student analyzing about 10 samples.

Our age estimates for the record are tentative and come from a broader study of peatland development at the site by Ireland and Booth (2011).  We will discuss our paleoecological record in class tomorrow, along with the Ireland and Booth study, emphasizing the implications for understanding long-term wetland development and hydroseral succession.

Advertisements

The microbes are in charge (Pymatuning Wetlands 2015, Day 4)

A student clearly trying to closely examine the obligate anaerobic bacteria.  Or he fell in a hole.

A student clearly trying to closely examine the obligate anaerobic bacteria. Or he fell in a hole. (Photo: AS)

After a breakfast of energy-rich waffles, the Pymatuning wetlanders slowly descended the rungs of the redox ladder into the world of wetland biogeochemistry. The microbes rule this world, and we examined the ways they make living by examining nitrogen, iron, manganese, sulfur, and carbon cycling in wetlands.  Electron acceptors, photosynthesis, oxidation, reduction, aerobic respiration, diffusion, mineralization, nitrification, denitrification, sulfur bacteria, photosynthetic sulfur bacteria, redox potential, ferric iron, ferrous iron, nitrogen fixation, sulfer-reducing bacteria, extended glycolysis, heterotrophs, chemoautotrophs, facultative anaerobes, obligate anaerobes, methanogenic bacteria, cation exchange capacity,  and other trophic-genic-ifications until our brains were full and it was time to cool off in the marsh.

Collecting vegetation cover data at Pymatuning Creek Marsh.

Collecting vegetation cover data at Pymatuning Creek Marsh.

We spent much of the afternoon at Pymatuning Creek Marsh, where the students established transects along the moisture gradient from the edge of the wetland to the interior, and quantified the distribution of vegetation, water-table depth, and pH. While the students collected data I had a little time to quietly explore the marsh a bit, and I took a few pictures…

My least favorite organism in the marsh today. (Deer fly, Chrysops sp.)

My least favorite organism in the marsh today. (Deer fly, Chrysops sp.)

It was hotter than yesterday and the deer flies (Chrysops sp.) were relentless. Much blood was lost. But we obtained the necessary data and managed to collect a few more plant specimens.  This group of students has a fantastic attitude and they are all quite a lot of fun. We returned to the lab to press plants and sort out the unknown plant species that they encountered along the transects.

Tomorrow we will explore the lacustrine wetlands of Pymatuning reservoir, and visit a swamp and some shallow water environments to round out our “must-know” plant list for the first week of class.

And a few more students are now contributing to the twitter feed: #PLEwetlands

"I'll carry this because it looks cool"

“I’ll carry this because it looks cool”

Tweeting from the field 2014, Ecology (#ees152)

Last day of ecology class tomorrow 😦

Some pictures from the fun this year…..

So many bugs, so little time (PLE day 9)

Picking macroinvertebrates.

Picking macroinvertebrates.

After discussing freshwater marsh ecology and seed banks this morning, the Pymatuning wetlanders headed into the field to sample macroinvertebrates. We will be comparing the composition of macroinvertebrate communities in several shallow ponds and marshes, at both closed-canopy and open-canopy sites, as well as shallow ponds with and without fish. We spent the afternoon picking the macroinvertebrates out of the samples, and identifying and counting them. So many bugs! Going to be more work to do tomorrow.  More pictures at end…

 

Experiential learning on the Tangled Bank: plant traits and ecological succession

An opportunity for experiential learning

Student reads a handout describing the objectives of a botanical survey of the "Tangled Bank" on the campus of Lehigh University.

Student reads a handout describing the objectives of a botanical survey of the “Tangled Bank” on the campus of Lehigh University. Photo: Christa Neu

In 1967 Lehigh University Professor Francis Trembley convinced the university to stop mowing a small area of the campus.  Professor Trembley named the area the “Tangled Bank,” and it became a place where students could observe nature right outside the classroom.  At one point in time there was even a “Tangled Bank” sign on the slope. One year after the mowing stopped, Professor Trembley had the foresight to encourage an undergraduate student to collect and identify all plant species growing on the Tangled Bank, and these collections were archived in the Lehigh Herbarium.  Read the full story of the Tangled Bank here (including some great recollections of Lehigh alumni in the comments).

A student identifies trees growing on the Tangled Bank.

Students identifying trees growing on the Tangled Bank. Photo: Christa Neu

In the fall of 2013, the Lehigh University ecology class (EES-152) completed a botanical survey of the “Tangled Bank” to document what species occupy the site today. The primary objective of this project was for the students to use their data in conjunction with the 1968 plant collections to assess how plant characteristics, such as functional and life history traits, change from early to mid succession. In addition to learning about plant traits and succession, the project allowed students to learn how to apply some commonly used statistical tests to assess differences between groups. They also compared their tree data to a similar dataset collected earlier in the semester from the Lehigh University Experimental forest, a forest that has undergone secondary succession for approximately twice as long as the Tangled Bank. The goal of this comparison was to assess how tree density and biomass change with succession.

Students conducting a botanical inventory of the Tangled Bank.

Students conducting a botanical inventory of the Tangled Bank. Photo: Christa Neu

Student conducting a botanical inventory of the Tangled Bank.

Student identifying a tree on the Tangled Bank. Photo: Christa Neu

Functional traits and succession

What are functional traits?  Functional traits are characteristics of species that strongly influence performance, and are therefore fundamental to survival and reproduction. They can be physiological, morphological, or represent reproductive strategies (the latter are oftentimes referred to as life history traits).  Plant functional traits include things like photosynthetic pathway (C3, C4, CAM), growth rate, shade tolerance, number of seeds, size of seeds, growth form, life span, seed-dispersal method, and seed viability. Functional diversity (i.e., the total diversity of these traits represented by an ecological community) is increasingly used as an important measure of biodiversity.

What functional traits might be expected to change with ecological succession?  Shade tolerance, growth form (herb versus tree), and lifespan might be a few of the more obvious traits that would be expected to change, as plants of later succession include many long-lived trees that compete for light. However, changes in other traits with succession may be less obvious.  For example, how might you expect seed viability (i.e., the length of time a seed can survive in the soil before germination) to change with succession?

Students pressing a collection of Tulip polar (Liriodendron tulipifera) from the Tangled Bank.

Students pressing a sample of tulip poplar (Liriodendron tulipifera) from the Tangled Bank. Photo: Christa Neu.

Field work

The Tangled Bank was divided into nine plots, with 3 or 4 students responsible for a complete botanical inventory of each plot.  The students identified and estimated the abundance of all plant species, and collected voucher specimens for archival in the Lehigh Herbarium. For trees, diameter at breast height (dbh) was measured and used to calculate total basal area of the forest and the average basal area per tree. The total density of trees (per 1000 m2) was also calculated.  Each group submitted an excel spreadsheet with their inventory results and dbh measurements as the first deliverable for the project.

Table 1. Common plant species of Lehigh University's Tangled Bank in 1968 and in 2013 after 45 years of ecological succession.

Table 1. Common plant species of Lehigh University’s Tangled Bank in 1968 and in 2013 after 45 years of ecological succession.

Pressing and identifying plants from the Tangled Bank.

Pressing and identifying plants from the Tangled Bank. Photo: Christa Neu

Data compilation and research on plant traits

The data from each group were combined into a class dataset. Students developed lists of the dominant species that grew on the bank in 1968 and 2013 (Table 1, above). Each student was then assigned two plant species (one from 1968 and one from 2013), and required to gather information about the attributes of these species, particularly with respect to life history and functional traits. The objective was to compile as much quantitative and semi-quantiative information on the traits of these species as possible.

Collecting Japanese Barberry on the Tangled Bank.

Collecting Japanese barberry (Berberis thunbergii) on the Tangled Bank. Photo: Christa Neu

The students could use any source of information for this research, as long as they documented their sources; but, we anticipated that they would rely heavily on some of the publicly available databases that have been developed to describe plant characteristics. Unfortunately the shutdown of the US government prevented access to several of these resources, and limited the number of traits that we could examine. However, the students made the best of the situation and found as much information as possible using a variety of sources. They submitted their research in the form of an excel spreadsheet (a template was provided to help standardize the data collection), and this was the second deliverable for the project. Information on sixteen characteristics were found for most species, so we focused our analyses on these (Table 2).

Table 2. Comparison of sixteen plant characteristics of early and mid successional plant communities on the Tangled Bank. Significant differences are denoted with asterisks (*p<0.05, p<0.01**)

Table 2. Comparison of sixteen plant characteristics of early and mid successional plant communities on the Tangled Bank. Significant differences are denoted with asterisks (*p<0.05, p<0.01**)

Data analysis and results

Student measuring dbh of tree on the Tangled Bank.

Student measuring dbh of a tree on the Tangled Bank. Photo: Christa Neu

Students then performed chi-squared and t-tests using excel, for categorical and continuous data respectively, to assess differences in functional traits of the plant communities in 1968 and 2013 (Table 2). The results of these statistical tests were submitted by each student as the third deliverable of the project.

The students found that plants of early succession generally had smaller seeds, longer seed viability in seedbanks, lower age of first flowering, shorter lifespans, smaller maximum height, and smaller average leaf area (Table 2, Figures 1 & 2).  The mid-successional plant community had a greater percentage of species with seed dispersal via mammals and birds (Table 2, Figure 1). Early successional species also tended to be less tolerant of shade and were likely more readily eaten by vertebrate herbivores than those of mid succession (Table 2, Figure 1). Plants of early succession also tended to produce more seeds per plant and have faster growth rates, although differences in these two traits did not meet our threshold for statistical significance (p<0.05). Also, no significant differences were found between N-fixation capacity, frost tolerance, fire tolerance, and drought tolerance of plants in the two communities.

Comparison of the frequency of selected plant traits in 1968 and 2013 on the Tangled Bank.

Figure 1. Comparison of the frequency of selected plant traits in 1968 and 2013 on the Tangled Bank. Results of Chi-squared tests are shown in Table 2.

Box plots showing the distribution of selected quantitative traits for plants occupying the Tangled Bank in 1968 and 2013. These plots show the distribution of values for each plant community, with the median indicated with a horizontal line, the boundaries of the box indicating the 25th and 75th percentiles the whiskers indicating the 10th and 90th percentiles, and outlier points shown with dots.

Figure 2. Box plots showing the distribution of selected quantitative traits for plants occupying the Tangled Bank in 1968 and 2013. These plots show the distribution of values for each plant community, with the median indicated with a horizontal line, the boundaries of the box indicating the 25th and 75th percentiles, the whiskers indicating the 10th and 90th percentiles, and outlier points shown with dots. Results of T-tests are shown in Table 2.

Connecting functional traits to Grime’s life history classification

Figure 3. Species of the Tangled Bank plotted according to  Grimes life history classification. Species positions estimated based on functional traits.

Figure 3. Species of the Tangled Bank plotted according to Grime’s life history classification. Species positions estimated based on functional traits.

Various life-history classification methods have been developed by ecologists to facilitate thinking about how species are adapted to environmental conditions. J.P. Grime proposed one such classification scheme for plants, which focused on adaptations to the amount of disturbance (i.e., processes that destroy biomass) and stress (i.e., external constraints that limit the rate of production) in the environment.  Under conditions of frequent disturbance, ruderal (i.e., “weedy”) species tend to be favored. Under conditions of high stress, plants tolerant of environmental extremes (e.g., cacti, carnivorous plants) tend be favored. Under conditions of low stress and infrequent disturbance, competitive species tend to be favored.

As an example of how species during succession may fit into Grime’s classification scheme, the functional trait data from the Tangled Bank was used to develop indices related to competition, stress, and disturbance.  Traits that would likely be selected for in these different environments were grouped and each species was given a composite score for each of the axes shown in Figure 3 based on the total standardized score of the grouped traits. The figure highlights the shift from ruderal species to better competitor species that has taken place between 1968 and 2013 on the Tangled Bank (Figure 3).

So many plants.  So many more plant traits. Photo: Christa Neu

So many plants. So many more plant traits. Photo: Christa Neu

Changes in tree density and biomass with succession

Tree data from the Tangled Bank was compared with data that the students collected earlier in the semester from the Lehigh Experimental Forest. Tree density (trees/1000m2) was higher on the Tangled Bank than the Lehigh Experimental Forest, and the average size (basal area) of trees was larger in the Lehigh Experimental Forest  (Figure 4A, 4B). Total basal area of trees, which takes into account both density and size, was greater on the Tangled Bank (Figure 4C).

Figure 3. Comparison of A) average basal area per tree, B) Tree density, and C) Total basal area of all trees for the modern Tangled Bank and the Lehigh Experimental Forest.

Figure 4. Comparison of A) Average basal area per tree, B) Tree density, and C) Total basal area of all trees for the modern Tangled Bank and the Lehigh Experimental Forest.

For the students, deliverable #4. Due 8 November. Complete the following tasks/questions:

1) In less than one page (single-spaced), summarize the differences in functional traits of plant communities in early and mid succession on the Tangled Bank. Your summary should include descriptions of why the observed differences likely occur (i.e., processes).

2) What other functional traits, not included in our analyses, might be expected to differ between 1968 and 2013?

3) For many of the traits we were only able to obtain categorical values, and the number of categories we defined varied among traits. For example, dispersal mode had six defined categories whereas nitrogen fixation capacity only had two. Do you think that the number of categories and how we defined them affected our statistical results and interpretation? Using the original excel spreadsheet, create new, broader categories for a trait of your choice by merging data into a smaller number of categories. Does reducing the number of categories impact the chi-squared test result? Does it change the interpretation?

4) What are the assumptions of a t-test? Did our data violate any of these assumptions?

5) Describe the position of the 1968 and 2013 species in the Grime’s life history diagram (Figure 3). What traits do you think were used to develop the species scores along each axis? In other words, which particular traits would likely be most related to each axis?  Some traits could be favored under more than one environmental condition (i.e., high stress, high competition, high disturbance). Justify your answers.

6) Where would the species of the Lehigh Experimental Forest likely be place on Figure 3?

7) Assuming that the differences in successional age are solely responsible for the differences in the density, size of trees, and total tree basal area of the Tangled Bank and the Lehigh Experimental Forest, what specific processes likely resulted in these differences?

8) Your answer to #7 assumed that the only difference between the two sites was age, a common approach often referred to as space-for-time substitution. However, what other factors might contribute to the differences between the two sites?

9) Using the data in Table 3 (below), calculate the rate of change of tree density and average tree basal area in a) early succession (first 45 years) and in b) mid-succession (45 to 98 years). Explain what processes likely contribute to these different rates of change.

Table 3. Tree density, average tree size, and total basal area of the Tangled Bank and Lehigh Experimental Forest (data for Figure 4).

Table 3. Tree density, average tree size, and total basal area of the Tangled Bank and Lehigh Experimental Forest (data for Figure 4).

We may have the new STEPS building, but nothing beats a classroom like this one.

We may have the new STEPS building, but nothing beats a classroom like this one. Photo: Christa Neu

Smelling your way down the redox ladder: wetland ecology in a bottle

“The act of smelling something, anything, is remarkably like the act of thinking. Immediately at the moment of perception, you can feel the mind going to work, sending the odor around from place to place, setting off complex repertories through the brain, polling one center after another for signs of recognition, for old memories and old connection.” – Lewis Thomas

Students experiencing olfactory "thrills" while measuring dissolved oxygen and redox potential of soil microcosms after flooding. The rotten-egg odor was intense in several of these samples.

Students experiencing olfactory “thrills” while measuring dissolved oxygen and redox potential of soil microcosms after flooding. The rotten-egg odor was intense in several of these samples.

Incorporating multiple senses into the learning process is a hallmark of experiential learning, and has long been viewed as a successful education strategy.  In a classroom setting, combining activities like observing, listening, speaking, writing, and drawing can help students to acquire, synthesize, and reinforce their knowledge of the world.  In a field course, the senses of smell and even taste can also inform and enrich the educational experience. Smelling the twig of a black birch, the leaves of spicebush, the flowers of skunk cabbage, or the wonderful rotten-egg aroma of a salt marsh are ecological observations that lead to questions of “why?” and “how?”  Furthermore, the sense of smell seems to be strongly linked to memory, albeit in poorly understood ways (i.e., the Proust effect).  Incorporating these sorts of sensory experiences into laboratory and lecture-based courses is challenging. However, I recently discovered a laboratory activity that was developed to explicitly appeal to the students’ sense of smell.  Well, perhaps “appeal” is the wrong word here.  The activity nicely demonstrates some important aspects of wetland biogeochemistry, a topic that my wetland ecology students often struggle with, and it does this while providing some considerable olfactory “thrills.”

Setup of two experiments. Each experiment included six microcosms, flooded for different lengths of time. Six experiments were done in total, allowing us to assess the influence of sulfate and organic matter quality and quantity on biogeochemical changes induced by flooding.

Setup of two experiments. Each experiment included six microcosms, flooded for different lengths of time. Six experiments were done in total, allowing us to assess the influence of sulfate and organic matter quality and quantity on biogeochemical changes induced by flooding.

The lab was developed for a soil science class by R.S. Dungan, B.D. Lee, and C. Amrhein. It can be downloaded here.  A set of microcosms are created by the students, each containing a soil which is flooded for a different length of time. A simple gaslock is used to prevent oxygen from entering the microcosms. We used six microcosms, representing flooding durations of 20 minutes, 1 day, 7 days, 14 days, 21 days, and 35 days.  In the original activity, the soils were amended with a small amount of gypsum (for a source of sulfate) and nitrogen-rich organic matter (alfalfa).  Students then measure changes in dissolved oxygen, iron, nitrate, and the presence of hydrogen sulfide.

We modified and expanded the lab for an upper-level wetland science course.  For example, we ran experiments with and without an added sulfate source, approximating the chemical environments of a salt marsh versus a freshwater wetland.  Within each of these environments, we also tested the effect that organic matter quality and quantity had on the biogeochemical changes induced by flooding.  To do this, one set of microcosms contained no added carbon (i.e., only the carbon that was present in the soil), one was amended with alfalfa (low carbon:nitrogen ratio), and one was amended with Sphagnum moss (high carbon:nitrogen ratio). In addition to measuring dissolved oxygen, iron, and nitrate, we also measured sulfate, redox potential, and pH.  Changes in concentrations were plotted against time and redox potential.

Photographs of the microcosms, after 35 days, for the different experimental setups.

Photographs of the microcosms, after 35 days, for the different experimental setups.

The results were fantastic, and some are summarized in the video and figures below.  I learned a few things by doing this lab; in particular, I think that with a little practice I could estimate redox potential using only my nose.  Certainly that would be a great skill for a wetland delineator to have!

The short video includes repeat photographs of a single flask, and provides a nice visual summary of the observed changes. Too bad you can’t send smells through the internet…

Figure showing all the data collected by the class, with concentrations plotted against redox potential measurements. Below are student comments along the redox potential gradient.

Figure showing all the data collected by the class, with concentrations plotted against redox potential measurements. Below are student comments along the redox potential gradient.

Biogeochemical changes with soil flooding, showing selected data from the class. Soils included a small amount of gypsum as a sulfate source, and the three lines indicate the results with organic matter of varying quality and quantity.

Biogeochemical changes with soil flooding, showing selected data from the class. Soils included a small amount of gypsum as a sulfate source, and the three lines indicate the results with organic matter of varying quality and quantity.

Biogeochemical changes with soil flooding, showing selected data from the class. No sulfate source was added, and the three lines indicate the results with organic matter of varying quality and quantity.

Biogeochemical changes with soil flooding, showing selected data from the class. No sulfate source was added, and the three lines indicate the results with organic matter of varying quality and quantity.

Questions for the students

A. Write a paragraph for each of the following questions, citing the appropriate figures:

  1. Describe the sequence of biogeochemical changes that occured after soil flooding. What chemical transformations take place?  Why do these changes occur?
  2. Explain the observed differences between the experiments with and without the added sulfate source. Why did these differences occur? What implications do these results have for understanding energy flow in salt marshes and freshwater wetlands?
  3. What is the likely effect of organic matter quality and quantity on the pattern and rate of biogeochemical changes after flooding? Why?

B. Write a sentence (or  equations) for each of the following questions:

  1. Hydrogen sulfide was produced in the experiment that reached a highly negative redox potential. What other gases were likely produced first?
  2. What visual changes occurred in the experiment (added sulfate, low C:N) between day 15 and 20 (see video)? What caused these changes?
  3. Why does nitrate increase in the first few days? What process is taking place?
  4. If we allowed these experiments to continue longer, what gas might be released eventually?
  5. Write the chemical equations for the redox transformations involving oxygen, nitrate, iron, and sulfate.

Literature Cited

Dungan, R.S., B.D. Lee, and C. Amrhein. 1999. Stinking Mud: An Introductory Soil Science Laboratory Exercise Demonstrating Redox Reactions in Flooded Soils. J. Nat. Resour. Life Sci. Educ. 28:89–-92.

The most abundant vertebrate in the forest?

The one red-backed salamander that my assistant and I found on our “pre-class” field trip, which was on a cold Saturday afternoon.

Would it be too cold for salamanders?  After freezing temperatures on Friday night, my daughter and I took a brisk Saturday-afternoon hike through the Lehigh Experimental Forest.  Our objective was to determine whether any red-backed salamanders (Plethodon cinereus) were under the nearly 100 coverboards that Michelle Spicer (Lehigh graduate student) and I had put out in preparation for this week’s Ecology (EES-152) lab.

We didn’t find any salamanders under the coverboards.  Not a single one.  However, after nearly an hour of searching (my assistant insisted that we not give up), we managed to recover a sluggish salamander from deep under a large rock. Salamanders were very abundant a couple weeks ago, but the sudden cold temperatures had clearly sent them digging deep in the soil, which is where they survive the winter.  If only the cold snap had waited a few more days.  Michelle and I had to quickly develop a backup plan for Monday’s lab.

However, we got lucky.  Sunday was warm, and temperatures never dropped below the upper 50s during the night.  Rain on monday morning and afternoon probably helped a bit too, and as far as I could tell, the students didn’t mind getting wet. Collectively, they counted 84 red-backed salamanders  in approximately 2200 square meters of forest (almost 23,700 square feet). And that is a minimum estimate….we certainly missed some.  So, scaling up, our estimate of red-backed salamander density, based on this single sampling effort a few days after the first freeze, was about 382 per hectare (or ~155 per acre).  Therefore, there are likely well over 1000 red-backed salamanders in the Lehigh Experimental Forest.  The numbers might seem surprising, but our estimate is lower than other estimates from eastern North America forests, where densities greater than 1000 individuals per acre have been observed.  I suspect that if we had sampled a few weeks ago, our estimate would be much higher; in fact, I wouldn’t be surprised if there are more salamanders in the experimental forest than there are students at Lehigh University.

Students in the ecology course (EES-152) collecting information on red-backed salamander density in the Lehigh Experimental Forest. Fall 2012. (Photo: RK Booth)

In addition to salamanders, the students collected information on the density of earthworms, using the liquid extraction technique. They will use the combined dataset, along with some additional observations and measurements, to test whether salamander and earthworm abundance differed between areas of the forest with deciduous (tulip poplar and sugar maple) and conifer (white pine) canopies.  How might the type of canopy influence the abundance of earthworms?  How might it influence the abundance of salamanders?  Or perhaps the forest vegetation doesn’t matter at all?  Hypotheses?

Below are a few pictures and video clips of the fun….

-rkb-

%d bloggers like this: