Final Thoughts

            As the ecosystem ecology course comes to a close, I am amazed at how much I have learned. I entered this course without the physics, GIS, and advanced bio/geo/chemistry requirements that were posted on the syllabus. It was intimidating to say the least. The first Dinamica assignment seemed like a foreign language and I was doubtful in my ability to pick up the new skill. I stuck with the class because the environmentalist in me was not satisfied with sitting around in circles and discussing rooftop gardens. I find it necessary to know and understand the cycles and endless functions of ecosystems, if I am going to effectively defend and protect them in the future.

            My interest in community gardens has proven to be a prime example of Integrated Natural Resource studies. I was drawn to the INR major without realizing how great it really is for me. From qualitative research interviewing community garden participants all the way to GIS modeling for finding new areas for community gardens; I have explored many ways of promoting this one form of sustainability. It feels good to be an ecologist and an advocate, someone who can legitimize the claims they stand for.

            As someone who is optimistic, hopeful, almost idealistic, it becomes crucial to ground myself in reality. I am graduating in a couple weeks and I am constantly wondering how will I make an impact on the world? How can I translate all I learned in the Rubenstein School of Environment and Natural Resources into applicable work? With an integrated major, a love for ecology, and a passion for the people that inhabit this planet, the options are endless.

            Ecosystem ecology provides a concrete opportunity to make a difference. Programs, like Dinamica, allow for people around the world to manage their environments for the best environmental practices and the optimal social equity. I am so happy I learned even a little bit about computer modeling, and I am pleased with the skills I have gained.

            I want to thank Gillian Galford for leading me through this challenging course. Also, thanks Tom Hudspeth and Eric Gottesman for being the two people who read my blog. I may even keep up with this past graduation as I am working in the forests performing research for William Keeton this summer. Ecosystem ecology is a theme that will be with me forever, so the blog will still be relevant.

As far as plans for after the summer, you’ll have to stay posted and feel free to shoot me emails with job opportunities: aviva.j.gottesman@gmail.com

Dataset Dreamin

            At this point in my modeling project on community gardens in Burlington, I am waiting on data from the Chittenden County GIS department.  Once I receive the files, I will convert them to Rasters so I can use them in my Dinamica Model. I have gone through the layers of data that Chittenden County provided on a map viewer online, and determined the datasets that are needed. These can be found below.

Dwelling Unit Per Square Mile from http://ccrpcvt.org/

Bus line data

Holistic Carbon Management Approaches

          This summer I will be on a field crew for UVM professor William Keeton (and our very own Sarah Ford-thank you thank you) and so I decided to learn more about his research this week. I watched a video of Professor Keeton (Bill) give a presentation at a Gund Tea (Gund Institute of Environmental Economics). The lecture was titled,  "Toward a Holistic Carbon Management Approach". Bill explained that deforestation in the tropics accounts for almost 15% of global green house gas emissions. Forest regrowth compensates for some of the loss through requestering the carbon, making global forests a net carbon sink (taking in more carbon then emitting). In the lecture, Bill asks “how can we enhance the strength of forests as a carbon sink?” Can active forest management promote carbon sinks and if so, what type of management?

            The three possibilities of management for carbon sinks include: passive management, reduced harvesting intensity, and intensified forest harvests. The first option requires conserving old growth, high biomass forests, through managing them as reserves and protected areas. In the Adirondacks, it was shown that biomass accumulates at higher levels over longer timeframes. However, if we do protect more forest land as a carbon sink, we must question the effects on a global scale. Are we displacing harvest impacts to another part of the world? If there is less wood being used, what are we using as a substitute? And is it actually more sustainable? Everytime we use wood instead of something else, we avoid emissions of other substances, however there is an assumption that it is a 1:1 substitution.

            The second option is a spectrum of different management scenarios that leads right into the third option of heavy management. Intense harvesting with short rotations all the way to long rotations with minimal harvesting. With the range of options, moving towards less intense harvesting can mean more carbon sequestering. Where to fall on the management scale really depends on the other ecosystem services in that setting. Carbon needs to be balanced with bird habitat, wood products, etc…the point is to maximize the benefits through combining all the approaches. Depending on the objective, passive and active management will need to work together to achieve different ends. This will take a new generation of carbon models that can track individual stands over time and simulate different scenarios to optimize how we manage large forests for carbon and for co-benefits.

           I am really excited to check out the variety of forest plots this summer and get further experimental education on the subject!

Lesson 8- REDD Case Study

Abstract

This lesson uses an econometric model as a tool for the REDD inititive which compensates developing countries for carbon sequestration. The model accounts for socioeconomic factors, using a spatial lag regression and a set of given parameters. A basic scenario of business as usual and a new, dramatic scenario are tested against each other in the model. The first model simulates the deforestation based on the econometrics for 2001-2020. The second model provides a carbon bookkeeping account of CO2 emmissions and deforested hectares, leading to conclusions for stakeholders in the region.

Introduction/Background

An econometric model is a a tool used to determine future aspects of the economy. It uses past relationships between parameters to statistically simulate the future, changing variables to create a variety of results.  An econometric model can be useful to simulate deforestation because socioeconomic factors on a local, regional, and international scale heavily influence the rate of deforestation. Some benefits of this type of modeling is that it takes into account a more compex, anthropogenic system. Economics cannot be ignored in environmental modeling, therefore econometric models can incorporate a more diverse aray of parameters. Some challenges are that it requires a lot more data collection, more statistical and mathematical effort, and a specific understanding of how local economics influence the environment.

REDD is an initiative on Reducing Emissions from Deforestation and forest Degradation in developing countries. The program was developed to provide incentives to developing countries to stop deforestation. If countries could reduce their deforestation to below a historical baseline they would be eligible for financial compensation from the international community, specifically from carbon credits. Modeling REDD projects would require an econometric model because as a country initiates policies to reduce deforestation, they are then making money that is going into projects that do not degrade the land. This is a relationship between economics and environment that requires complex, multi-level datasets.

A spatial lag is when neighboring cells affect one another in the model, due to the influence on one variable to the other. Deforestation results in more crop areas and denser cattle herds, while precent of protected areas decreases deforestation. Deforestation creates more roads and increases migration rates. The latter two may have a feedback loop that will continue to increase. To summarize, increasing protected  areas will decrease deforestation, while growth in the other variables results from and continues due to deforestation.

Figure 1- Simulation Model

The spatial neighborhood matrix incorporates the influence of the socioeconomic context of neighboring municipalities in the prediction of deforestation rates of a certain municipality. This means that the matrix is designed for each municipality to respond to the changing socioeconomic patterns around them. No municipality works in isolation, instead they react to one another. Deforestation will not occur within a single municipality; however it is most likely to create a spreading pattern between municipalities. The model will run for 20 iterations to represent 2001-2020.

Results

The final landscape has a slight increase in deforestation (light green) that fills in a lot of the patches from 2001. There is not a huge difference, but there is a bit of land use change occuring around the edges of where it was already occuring. It shows there is pressure to protect the land, and pressure to develop the land. The municipalities develop and deforest slowly, one affecting the other resulting in a spreading pattern, like a river delta. Intermediate years did not help show much of a trend because 2010 decreased deforestation and then after that it increased again. 

Figure 2- 2001 Land use map (left), 2020 Land use map (right)

The scenario I created was “0” growth in crop expansion and “1” or 100% growth in cattle expansion. This was so dramatic so that I could see the result of such a drastic change; however it was too much and made it difficult to observe the results. The new scenario results in a lot of cattle land in every municipality. Deforestation is vast and unruley. This is unrealistic, but demonstrates the potential for unlimited cattle expansion.

Figure 3- Land use map for scenario of 100% cattle expansion, 2001 map (left), 2020 map (right)

In the carbon bookkeeping model, the model uses 85% for the biomass emmission factor and 50% for the Biomass-Carbon conversion factor.  This model results in excel tables that display deforestation in hectares and CO2 emissions for each year. 

Figure 4- CO2 (top), Deforestation  (bottom)

I repeated it for scenario 2. It is clear in both examples that the CO2 emissions mirror the deforestation to some extent. 

Figure 5- CO2 (top), Deforestation (bottom)

They follow a similar rise and fall pattern, although the CO2 emissions continue to rise after deforestation ends due to a lag in the ecosystem processes. Because of the dramatic choices I made for scenario 2, the model stops at 2004. The deforestation declines and then stops because there is nothing left to deforest; meanwhile the CO2 emissions increase to a massive number of more then nine million tons. I recommend stakeholders do NOT expand at a rate of 100% for cattle or for crops. This will result in complete degradation of the land. 

Modeling for Community Gardens in Chittenden County

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            Community gardening is a versatile tool used to transform space, society, and individuals in a collectively cultivated land. Gardening can heal the body, mind, and spirit, as it connects humans with the earth. On a community level it improves urban health, provides for social inclusion, and allows for active civic participation. Mainly, it is used to promote practices of sustainable living in an urban environment. Organized garden projects allow for food systems to be visible in an urban landscape. They provide opportunities for learning about food production and wider food system issues (Pudup, 2008). 

            My modeling project seeks to find the most optimal areas of land for potential community gardens in Chittenden County, Vermont. Using data from Friends of Burlington Gardens, I will locate all existing community garden sites in the county. The datasets required for the model inputs are still being located. Some important contacts include Brian Voite and Austen Troy who have GIS data on Chittenden County land use. Walter Poleman is a contact for data regarding soils and vegetation. Finally, Jess Hyman from Friends of Burlington Gardens, and Andrew Schlesinger who interned for her doing GIS mapping, are both people to contact for more in depth information on sighting community gardens. 

            I will be using Multiple Criteria Evaluation (MCE) to determine the most favorable areas for developing new community gardens. The MCE process is often used in environmental impact assessments when development and conservation are both considered in a project. Through the use of functions like Groups and Calculate Distance to Feature Map, I can include all of the necessary criteria.

            After assessing the current community gardens, I will solidify a list of criteria that are required for garden sites. These may include characteristics like plot size, slope, soil type, and vegetation cover; in addition to access to bus routes, distance from markets or fresh food, distance from industial zones, walking distance from neighborhood, and general socio-economic factors. These variables will be translated into parameters for the modeling. By creating groups to address each criterion, the final product of the model will display a map of the possible locations for the new gardens.

Example of MCE modeling, using Group functors and the Region Manager functor

            Currently, Chittenden County has around 25 allotment style gardens, 12 of which are affiliated with Burlington Area Community Gardens. In addition, there are multiple group gardens, several school gardens, and neighborhood gardens. I will focus on allotment style gardens that provide plots for individuals to rent for the season. Allotment gardens are the beginnings of a very strong alternative food system in the region, therefore they are the kind of garden I will research. One challenge that may occur is that the gardens in Burlington may have different criteria than the more rural parts of the county. Additionally, Chittenden has a lot of urban land use compared to Vermont as a whole, so it may not be transferable to the rest of the state. In the end, I hope to have sighted potential plots in Chittenden for the creation of new community gardens.

Google Images picture of garden locations throughout Vermont, image is focused on Chittenden County

Unwise Evolution

Oh the joys of break: endless TV, pints of ice cream, and the thought of school lost behind the fog of drugs….Wait, what?

Well when you get your wisdom teeth out over spring break that is about all you can do. With an icepack to the face, eating blended bananas, the single scientific question I could muster was, why do humans grow teeth that do not fit in their mouth? After looking into the matter, it appears there are several contributing factors.

This is what my cheeks looked like.

The pain and suffering I felt from my wisdom teeth protruding through my too small jaw was, indirectly, a trade off for my big brain. Our early ancestors had big jaws and small brains, with weak jaw muscles allowing for the growth of the third set of molars. With bigger brains, we taught ourselves how to process our food, making mushier meals that had less wear and tear on our molars. With less weathering on the teeth, there is even less room for the wisdom teeth to grow in. Additionally, the processed diet reduces jaw growth, leaving no space for those molars. In the Proceedings of the National Academcy of Sciences, it is suggested that the shift to agriculture and the resulting change to our diets caused our jaws to become shorter, meaning less space for our teeth.

So why have we not evolved a mouth sans wisdom teeth? The problems associated with wisdom teeth, and dental crowding in general, are too prevalent and recent to be attributable to evolution. Additionally, dentistry is too effective for evolution to have weeded them out. Wisdom teeth do not grow through the gum until quite late in life, usually after people have reached the reproductive age, so there are weaker selection forces against them. Another explanation is that the genes responsible for molar growth are also important for other things, therefore they must not mutate and evolve.

Some Japanese scientists suggest that tooth pulp could be a good source of stem cells and an alternative to embryonic cells, showing there can be benefits to wisdom tooth extraction. 

This bit of wisdom does not help ease the pain of a throbbing mouth.

Exam Questions

Here are three exam questions and answers.


1. What conditions promote the Midwest plains?

Winds carry air up the windward side of the Rocky Mountains, the air cools, and water vapor condenses and precipitates. The West side of the Rocky’s is colder and wetter. The air then moves down the leeward side of the mountain, expanding and warming, thus increasing its capacity to absorb and retain water. This creates a desert like, dry ecosystem. The rain shadow effect extends 1,500 km to the East of the Rocky’s, creating a range from 300mm/yr in Colorado to 1,000 mm/yr in Illinois. The whole Midwest is within that rain shadow.

2. How does soil texture affect other soil properties? Why does it influence ecosystem processes so strongly?

The texture of the soil, which is based on the proportions of sand, clay, and silt, are indicators of the soil properties.  Soil properties include drainage, water holding capacity, aeration, susceptibility to erosion, organic matter content, cation exchange capacity, pH buffering capacity, and soil tilth. Properties like drainage and water holding capacity will determine erosion rates, nutrient leaching rates, decomposition, etc. The texture influences the properties, which have a direct effect on ecosystem processes because it determines the vegetation and natural communities that will grow.

3. How do bacteria and fungi differ in their environmental responses and their roles in decomposition?

Bacteria and Fungi are responsible for 95% of the total decomposer biomass and respiration. Fungi create networks of hyphae, which are filaments that enable them to spread and grow. This allows them to get nitrogen from one place and carbon from another. When soils are plentiful, they spread their network in search of the most labile substances. Fungi secrete enzymes which degrade cell walls, breaking down litter and extracting the nutrients. Bacteria have a small size and large surface: volume ratio that allows them to absorb soluble substrates, and to grow and divide quickly in substrate-rich areas. Bacteria often lines roots and enjoys macropores in the soil, where water movement is rapid and nutrients flow.