A Change in Directions for Future Plans



After graduating from UVM and spending some last precious days with college friends, I will be heading home to Ashland, Oregon.  Even though I will arrive in Oregon with a BA in Zoology, I have different plans in store.  I will be experimenting with a career in the performing arts area, primarily harp playing.


I have been playing the harp for almost 12 years now and have mostly just kept with it as a hobby.  However, this past summer I was a street performer on Church Street, and I thoroughly enjoyed it.  Not only were those passing by appreciative of my playing, but the income was also descent.  I was asked multiple times if I would be selling CD’s anytime soon.  Unfortunately, my repertoire contained several cover songs that I did not have the rights to record on a CD.  One couple actually danced while I played.  It was a very rewarding experience.


While I am interested in the animal sciences, this past semester made me think that perhaps a career in this field is not the best thing for me.  But I may change my mind.  In the meantime, I will be busking on sidewalks, handing out my contact info to those who wish to hire me to play at a wedding or party, and testing out storytelling.  If business is running slow, there is a college in my home town with a Zoology Department that may be willing to hire me for some part-time job.  I’ll see where the music takes me.


Ethics in Experimentation to Reduce Environmental Impact

In the scientific world, there is a tendency to conduct experiments with the only goal being to gain knowledge.  Sometimes the knowledge is just to understand a species or ecosystem better, other times it’s the hope to discover a benefit for humans.  In the past, ethics did not play a part in the field.  Now, within the past few decades, this issue has been brought forth with the welfare of the study organisms or environment in mind.  Some basic guidelines have been made with the intent to help reduce our impact on the organism’s suffering and our impact on the study sites.  There are so many studies done where species of animals or plants are wiped out from plots of land to see the impact this has on other species in the area or to see how well the area recuperates after this disturbance.  Ideally, only these plots are affected, but removing certain species may have a big effect on other species residing near these plots.  For example, removing major predators would increase the prey population or decreasing the prey population would also decrease the predator population.  Burning off grass in an area to see which seed colonize the land best out have the soil more prone to leaching and runoff, which would change the soil dynamic and the surrounding plants could be affected by this, not to mention the plants that move in after the disturbance.  It is still a hard question to ask where to draw the line between experiments that are valid and acceptable for the gain of knowledge versus it’s not worth the impact the experiment would create.  However, thankfully, the idea of at least trying to reduce a scientist’s impact is growing in popularity.

Project update

So, my project is very slowly progressing forward.  I have found maps of town boundaries, vernal pools, and railroad lines in Vermont.  I think I can use BioFinder to give a map of wooded areas in Vermont, but I still need to find a map of elevation in Vermont.  I have Excel data on which towns Jefferson Salamanders have been found in, and I would like to map this onto the map of town boundaries before making a decision on which town to narrow my project map on, but I may end up just looking at the data listed and pick a town with few salamander numbers because the computers in the computer lab don’t want to open up any of the maps I find.  Even Dinamica is being problematic.  But with Professor Galford’s assistance in converting the maps into a form that the computer lab will agree with, I should be able to get it to work.  However, I do not know how to map Excel data onto a shapefile with ArcMap, and the help sites on this are not very helpful in explaining this.  This upcoming week, I plan to find all the maps I need, decide which town to focus on, and start building my model for mapping winter and spring habitats, finding the best routes between them, and seeing if a railroad plays a factor in this migration route.

REDD case study (Lesson 8)






                  The goal of this lab is to learn how to use socioeconomic variables to project deforestation rates, and how to make a carbon bookkeeping model.  Gross rates will be converted into net rates, and the functors Calc Neighborhood and Calc Spatial Lag will be used.  Two scenarios were created to see if changing the rates of crop expansion and cattle growth affected the rates of deforestation.  The amounts of deforestation and CO2 emissions were higher when the rates of crop expansion and cattle growth were increased, but the trend of CO2 emissions had an uphill followed by a downhill trend.  Some unaccountable factor may have been in play.






                  A REDD (reducing emissions from deforestation and forest degradation) program was proposed to financially compensate developing countries in tropical areas for reducing deforestation and thus reducing CO2 emissions into the atmosphere.  Predicting deforestation rates in the future would have to factor in several variables including crop expansion, cattle growth, protected land areas, proximity to paved roads, migration rates, and neighborhood matrices.  This lesson will do just that by running an econometric model with a spatially-explicit deforestation simulation model.  An econometric model uses statistics to specify the relationship between economic variables such as those mentioned above.   This is helpful in factoring in the probability of deforestation occurring based on the economic factors.  It adds more than just calculating how fast land is cleared in a simulation model.  A separate model will map the predicted carbon emissions due to the deforestation.






1)     Open the model “simulate_deforestation_under_socioeconomic_scenarios.xml” from the folder “Examples/REDD_case_study”.  Notice that there is the input data within the Group “Inputs” located on the far left, the precalculation in the Group “Precalculation” to the right of the inputs, and then the simulation model within the Repeat functor. 



The inputs are the annual rates of crop expansion and cattle herd growth.  Expand the groups within this input group to look at what they hold.  One of the groups has the static variables of deforestation: percent of crop areas, cattle herd density, percent protected areas, proximity to paved roads, and migration rates.  Note that the values in each Double is 0.05, reflecting the rate of increase for crop expansion and the growth of the cattel herds.  These values will be modified later.Image

The precalculation group calculates the neighborhood matrix, the original forest extent per municipality, and the municipal areas.  This is to define which neighborhoods are municipalities and factor in socioeconimic factors in the neighborhood matrix.  With regards to deforestation, how fast a municipality expands for social or economic reasons would equally effect the deforestation rates.  Expand the For Each Region functor to see what it contains. Image

2)     Notice that the Group in Repeat has three For Each functors.  The first two update and calculate the annual rates of change for crop area and cattle herd.  Expand these two to see that they are set up the same way.



This information goes into the Calc Spatial Lag functor, which factors in spatial lag into the annual changes of the variables crop area and cattle herds, percent of protected atres, proximity to paved roads, and migration rates to calculate gross deforestation rates as seen in the input group.  Spatial lag is the lag in the use of an area for either of these variables.  This lag would lead to a lag in deforestation.  This data then goes to the third For Each, which changes the gross deforestation rates into net deforestation rates.  Expand the Calc Spatial Lag and the third For Each functor to see what they contain. Image

3)     Notice that the “Number of Iterations” in Repeat is “20” because this model will be running a simulation for each of the years between 2000 and 2020.  Run the model.  The output landscape maps for each of the years can be found in “Examples/REDD_case_study” as “ers” files.  To see how these maps differ if the crops expanded and the cattle grew at a slightly faster rate, change the value in both Doubles from “0.05” to “0.15”.  Run the model again under this second scenario.

4)     Open the model “carbon_bookeeping_model.xml” from the folder “Examples/ REDD_case_study”.  Image

This model will simulate carbon emission from deforestation by calculating annual deforestation on a map of the area containing forest carbon biomass.  It assumes that 50% of wood biomass contains carbon and trees release 85% of their carbon into the atmosphere when deforestation occurs.  After that, the model converts the carbon biomass into carbon emission.  Click on each input map to see the initial landscape and the forest carbon biomass.  The red and yellow areas in the biomass map contain high amount sof carbon while the blue areas in urban spots hardly have any.Image


5)     Notice that within the group in Repeat, there is the same Load Map that has the output maps from the previous model.  Since this is the case, go back to the first model and reset the Double values to “0.05” and run the model so that the carbon model will use the results from scenario 1 and its results will thus reflect the first scenario. Image

These maps are inside the Group to help keep the order of execution in the right sequence.

6)     Within Repeat, the identified annual deforestation cells along with their corresponding biomass is converted into carbon and then into emissions.  The output tables reflect the annual deforestation and the carbon emissions.  Run the model and look at the Excel data of both tables.  They can be found as “csv” files under “Examples/REDD_case_study”.  Note that the carbon mass has already been converted into units of CO2 in the “annual_carbon_emissions” table.  Go back to the first model, change the Double value to “0.15”, run the model, and then run the carbon emissions model again to get results for scenario 2.



                  The output maps from scenario 1 in step 3 from the years 2001 to 2020 shows a steady increase in land being transformed for socioeconomical usage.  In the interest of saving space, the maps for the years 2001, 2005, 2010, 2015, and 2020 respectively are shown below.  The dark green shows forested land while the light green shows urban and crop or pasture land.



The output maps from scenario 2 in step 3 from the years 2001 to 2020 also shows a steady increase in land being transformed for socioeconomical usage, only the transformation is faster than in scenario 1.  The years 2001, 2005, 2010, 2015, and 2020 are shown respectively below.



The output tables for scenario 1 in step 6.  The first table of annual carbon emissions shows a general increase in CO2 emissions from the years 2001 to 2020 and the deforestation shows varying amounts of hectares of land undergoing deforestation occurring from the years 2001 to 2020.



The output tables for scenario 2 in step 6.  The first table of annual carbon emissions shows higher CO2 emissions than in scenario 1 from the years 2001 to 2011.  From 2001 to 2008 these emissions increased and then in following years decreased until in 2020 the emission is lower than in 2001 for either scenario.  The deforestation table shows varying amounts of hectares of land undergoing deforestation occurring from the years 2001 to 2020.  Overall the deforestation amounts are higher than in scenario 1 until the year 2020 when it reaches a low value that not even scenario 1 had.




                  Between scenarios 1 and 2, it was predicted that increasing the crop expansion and cattle growth rates in scenario 2 would lead to an increase in the rates of deforestation as the demand for land for crops and cattle increases and thus also increase CO2 emissions.  They were not the only factors for urban expansion and deforestation, but changing the rates of these two factors would supposedly have an effect on net deforestation over consecutive years.  The deforestation maps supported this theory by showing urban growth in both scenarios, and the net amount of deforestation in scenario 2 was higher than in scenario 1.  The deforestation tables also reflected that more deforestation occurred in scenario 2 than in scenario 1.  However, the rate of deforestation varied from year to year in both scenarios and there was no consistent up or downward trend.  In general, with each year in both scenarios, CO2 emissions went up if deforestation increased or down if deforestation decreased in comparison with the previous year, but this is just a correlation.

                  However, the trends of the CO2 emissions were different in each scenario.  There was a general upward increase over the 20-year period in the first scenario, but in the second scenario there was an uphill trend to the year 2008 followed by a downhill trend.  Since the deforestation data did not have a specific pattern, I cannot map the CO2 trends with deforestation.  Perhaps even though the crop expansion and cattle growth rates were increased, other socioeconomic factors might limit how far the expansion of land use for crops and cattle is allowed to go.  Maybe the human population growth cannot keep up with these growths, and thus they do not need all the excess food, and so less land is allowed to be deforested for crop and cattle usage.  There may also be some other factor that comes into play when these rates are increased, but I cannot account for it.  These models do well to display deforestation and CO2 emissions, but they may not account for all factors or explain all possibilities.

Jefferson Salamanders and Railroads


      For my project, I’m interested in the migratory paths of the Jefferson Salamander in Vermont.  These salamanders are rare in Vermont and inhabit different environments in the winter and spring.  Namely, they hibernate under leaves in upland wooded areas in the winter and they breed in lowland vernal pools or wetlands in spring.  With these salamanders being about six or seven inches long, these two habitats need to be fairly close together, and any obstacles in the way, such as a road, would inhibit the continuation of the population in an area if it led to the salamanders not being able to cross to the pools to breed.  These guys only move at night during or just after a warm rain, so their migrating window is fairly short and they are vulnerable to desiccation if they do not reach the pools in time.

      In my project, I will hopefully discover if there is an area where the species is vulnerable to such a problem because a railroad is obstructing the spring migratory path.  Railroads present an interesting obstacle because unlike roads in which the main danger is being run over, a railroad track acts as a wall that cannot be passed unless there so happens to be space under the track for the salamanders to squeeze through or a road crossing the railroad track offers a sort of “bridge” to the other side.  If so, I should be able to determine where some sort of underpass or culvert should be built under the railroad in order to allow the salamanders to move freely to their breeding pools.



      In order to solve this, I will first identify the winter and breeding habitats of Blue-spotted Salamanders.  The breeding pools can be found fairly easily through BioFinder, but for the wooded winter habitats I will have to create a model finding such areas with the factors of elevation and land cover/type with the help of BioFinder or VCGI.  Based on the habitats found, how close they are to each other, and an online map of the known distribution of Jefferson Salamanders, I will be able to focus my project within a certain county or town. 

      I then need to model the shortest, best routes between the two habitats.  We’ve done a similar model in one of our previous lessons using roads to connect towns to a highway, but I will be including undesirable areas that should not be crossed on the way to the pools.  For example, urban areas would not be good crossing areas.

      Once the best route is picked, I will look to see if there is a railroad that intersects this migratory path.  In a model, I will be displaying the ideal migratory and the railroad intersection to see how large the intersection is.  The migrating path could be a quarter mile or three miles wide.  Depending on the size of the intersection, I will then need to decide in my discussion how many culverts are needed and how far apart they should be.




The Oregon Dunes


When I went home for Spring Break in Oregon, my friends and I drove along the coast and we came across the Oregon Dunes.  This is a well known tourist attraction in this state, but it’s also interesting how they form.  In general, dunes are formed with wind or water flow, but in the case of coastal dunes, both are needed.  Strong waves push sand onto the shore, and as it accumulates, wind blowing in from the ocean pushes the sand inland.  However, it is vegetation or rocks that prevents the blown sand from moving further inland, and thus sand begins to accumulate at these spots and dunes are formed.  However, the wind constantly continues to carry new sand to these spots while at the same time pushing sand on the windward side of the dunes onto the leeward side.  Young yellow dunes are well drained and dry, but they contain calcium carbonate from seashells that have been pushed onto shore, and seaweed that have been brought to shore by waves decompose and add nutrients so that grasses accustomed to such harsh environments such as salt water sprays and little water can grow.  The deep roots of these grasses add nitrogen to the soil and help hold the sand together, so the dunes grow bigger.  The addition of nitrogen allow other plants like heather to grow.  Heather adds humus to the soil, and when there’s enough humus, coniferous trees can grow on these now grey dunes.  The Oregon Dunes are in the older stages with a few “islands” of coniferous trees.  These dunes are also popular for driving four-wheelers.Image

Test Questions

What is albedo and how does it differ in a tropical area as opposed to the Arctic?

      Albedo is the reflection coefficient of solar energy from the land surface.  The higher this coefficient, the more solar radiation is being reflected back into the atmosphere.  In areas of high vegetation, the fauna help to absorb most of the solar energy, so the albedo is low.  Thus, albedo is low in a tropical area due to the high amount of vegetation.  In the Arctic, there is practically no vegetation and all the ice plays a major factor in reflecting solar radiation, so the albedo is high.


Why does the E soil horizon only form in really wet areas and not in dry areas?

      The E horizon is the soil horizon where the maximum leaching has occurred.  For leaching to occur, there must be water present to drip the nutrients away from this horizon and into deeper layers.  In dry areas, there is not enough water for this to occur.  Even in areas that receive a fair amount of water, it takes a lot of water to leach a soil to the extent that this E horizon is formed, so this horizon is only found in areas that have a lot of water such as a swamp.


If you have a compost pile outside and hope to use it in time to fertilize your garden, would you want Earthworms anywhere near this pile?  Why?

      Yes, worms would be good to have near and in the compost pile.  Earthworms are decomposers, and thus help to decompose dead organic material.  As they eat the material in the compost pile, they help speed up the decomposition process of this material.  Worms also serve to help mix this material and the soil.  The result in time is nutrient rich soil formed more quickly for use in the garden.