Looking into the future

I have spent the past three or so blogs focusing on my project and having written a rough draft of my paper for today, the last thing I want to do is write more about it. Therefore, I am choosing to write about a more positive subject - my future. Beginning May 7th, I will be a crew leader leading a crew of students based out of Lake Umbagog National Wildlife Refuge in New Hampshire. My crew and I will be doing bird and vegetation surveys in spruce-fir forests to see how birds respond to different forest management techniques. Since this job is outside in the woods everyday all day, you can probably guess that this job has a lot of connections to ecology. Everything about this job, actually, is ecology. I'm mostly excited for the data gathering part of this job because I get to walk around the woods and keep track of all the birds I hear and see so I get to test my bird identification skills which I love. It will be interesting to see the different types of birds present in these types of forests because I haven't spent much time in spruce-fir forests. I'm curious to see how these interior forest birds are different from birds that live on the edge of forests and in more urban areas. But the results from the research we are doing will also be very interesting. I am curious to see the birds responses to different forest management techniques and if that has any effect on how people decide to manage the forests. It is safe to say that ecology will be on my mind every day of my three months on this job, especially because I'll essentially be living within the spruce-fir forest ecosystem being in a remote area without cell service or internet! I think this class is what has put these things in my mind and has given me a better understanding of the natural world around me.

Beginning of introduction - deer info

Many people firmly believe that hunting is an essential management technique for the white-tailed deer population in the Americas. There are many subspecies of White-tailed deer, mostly geographically distinguished, but the overall species currently inhabits almost all of North America and Central America and parts of South America and Canada. They range from as far north as Prince George and British Columbia in Canada and as far south as Peru in South America. Their total population size is estimated to be 25 million. White-tailed deer are able to survive in a variety of habitats from large woods of the north to deep saw grass and swamps in the south. They also inhabit farmlands, cactus and thornbrush desert areas. The ideal habitat for white-tailed deer is in dense forest for cover, but near edges for food. Deer are herbivores, but feed on a variety of plants including buds and twigs of many tree species, cacti in the south, and other tough shrubs that other mammals would avoid. Their ability to survive and adapt in a wide variety of habitats and their ability to forage on a wide range of plants makes it easy for this species to thrive. The constant development caused by humans increases the amount of edge present in the landscape which destroys habitat for many other mammal species, but it has little effect on the deer populations which makes it easier for white-tailed deer to outcompete other mammal species that need large forests.

White-tailed deer are native to North America, but their populations have not always been as large as they are today. They have an immense influence on the landscape as they change the composition of plant communities through their foraging and can often lead to over-browsing where their population is large.  This can cause serious damage to the vegetation of forests and can also be destructive for crops, vegetable gardens, and ornamental plants when there are white-tailed deer living in close proximity to humans. Deer are also a large cause of car accidents and can cause serious injury to the occupants of vehicles. They also serve as important vectors for disease such as Lyme disease because they serve as hosts to the ticks which carry the disease. These are all very real problems that having a large white-tailed deer cause.

In Vermont, deer are found in mountainous areas, river valleys, agricultural lands, forests, and even in backyard suburbs. In their northern range, deer yards are a critically important habitat in order for them to be able to survive the winter. Only 7-8% of Vermont’s forests make up this critical habitat type and is possibly a limiting factor for their population growth. They may move 10 to 15 miles to reach a deer yard. Wintering deer yards must contain evergreen trees to catch the snow in their branches and keep the snow from getting too deep. The trees also provide a thermal cover to protect the deer from wind and below freezing temperatures. Deer hunting in Vermont is currently legal and thousands of people partake in the activity. Some hunters hunt for sport, while many others believe that it is an important way to control deer numbers which is important because an over-abundance of deer causes many problems. Hunting is a way to balance the needs of the deer and the needs of the people and is an important element in the cultural heritage of Vermont. The regulated deer hunting seasons in the fall of each year is how deer numbers are currently managed.

Project Project Project

Well, my project is still confusing to say the least. I think I've realized that figuring out the model set up is the part that is confusing me the most and that once I figure that out, I’ll know more about what to do with the data I need. I think my model is similar to lesson 5, the multiple criteria evaluation because I will need to have a calc categorical map for each environmental factor (land cover, slope/elevation, wintering areas, etc.) with an equation that will produce a categorical map with rankings (0-5 maybe?) for each cell on the map. Then all the output maps from those calc categorical maps should be combined to create an overall habitat suitability map where each cell is ranked (again 0-5) for habitat suitability for white-tailed deer. This habitat suitability map will determine where the deer population will spread as time goes on. Eventually the current population growth rate will have to go into the model to determine what the population will look like with continued hunting but I will also have to use some sort of equation to determine what the population would look like if there was no hunting. I know the biggest limitation after hunting is winter severity so if I find out how much that is effecting the population then I can use that to help with figuring out what the population growth rate would be without hunting. Not quite sure how to figure that out though. I think I need to set up another time to talk about it with prof. Galford!! 

White-tailed deer project blog #2

My project is slowly beginning to come along. I was able to get a GIS layer of the town boundaries in Vermont that contains the harvest rates for each town for 2010, 2011, and 2012. I also have a WMU (wildlife management unit) boundary layer for the state of Vermont. This layer contains a column showing the number of hours hunters logged during the annual deer hunter survey to act as a proxy for overall hunter distribution/effort. Along with the GIS layers, I have the current deer population estimate (pre-hunt) by physiographic region and WMU and the sighting rates for each region/WMU and statewide from 2003-2012. Luckily, there is a very cooperative deer project leader working for Vermont Fish and Wildlife who is more than willing to give me any deer information I need for my project. From the information given to me, I have the hunting kill rates per WMU which comes from the success rates and the number of permits distributed. To explain the differences in kill rates and success rates between the WMUs is likely based on multiple environmental characteristics. These would be the same environmental factors that are influencing the differing deer populations in each WMU or town. These environmental factors include land cover (forest or not), proximity to edge, slope/elevation, proximity to deer wintering areas, and possibly distance to roads and rivers. I think this would make my model a type of multiple criteria evaluation. To account for these factors, I think I would need multiple calculate map functors with equations for each of these factors to determine the habitat suitability of each cell of an input landscape map of Vermont. Maybe I could get the output to be a categorical map with values from 0 to 5 ranking the habitat suitability of each cell based on the equations.

As for the deer population simulation without hunting, the biggest non-hunting factor impacting deer mortality is winter severity. This needs to be taken into consideration when trying to determine how the population will grow and spread without hunting pressure. In this case, it may be important to consider historical WSI recordings and the region specific regression equation that the deer project leader can provide. This is all the information I have so far on my project. It’s not much, but it’s definitely a start and it is slowly becoming less confusing and daunting.

Lesson 8 - REDD Case Study

This lesson shows us how to project deforestation rates based on socioeconomic variables. An econometric model specifies the statistical relationship between economic variables pertaining to a particular economic phenomena, in this case deforestation. It is useful to use an econometric model to simulate deforestation because it takes into consideration the many complex dynamic phenomena that produce deforestation as a result. These types of simulation models are important for evaluation of the potential for future deforestation under a business-as-usual scenario. In the case of this example, this is important in order for countries to receive incentives from reducing their carbon emissions from deforestation. REDD stands for “reducing emissions from deforestation and forest degradation” and is an idea that developing countries who do this over time, can receive financial compensation for doing so. This would require a country’s emission levels to be lower than historical levels and would require them to know historical emission levels and whether they were high or not. A model like this is important to predict future deforestation rates based on multiple economic variables to see if receiving compensation for REDD is a possibility for certain countries.

                The model used for this lesson contains a spatial lag regression that is applied to compute the influence of five variables on deforestation: Percentage of crop areas, cattle herd density, percent of protected areas, proximity to paved roads, and migration rates. The five variables listed are the five static variables that are used to determine the probability of deforestation. The spatial lag regression makes it so that the deforestation does not happen right off in respect to the spatial variables and instead occurs over time and spreads over time. This models “spatial neighborhood matrix” allows neighboring cells, in this case municipalities, to have an influence on the cell in question. This means that each cell is not independent of the surrounding cells and the variables in the surrounding cells will effect it. In terms of deforestation, this means that if one cell becomes deforested, then it is likely that neighboring cells will become deforested as well over time. Because of this, we would expect to see deforestation occur in expanding clumps instead of in random patches.

                After running the model with various cattle herd and crop rates, it was determined that the smaller the rates the better because this will produce a lower, steady rate of deforestation and CO2 emissions. A high rate of cattle herd and crops means a rapid increase of land use and therefore a rapid increase in deforestation and CO2 emissions and an overall unstable state.

                The model is simulated for 20 years and a map output is produced for each year. Below are three output maps from the model starting with the first year, the 10^th, and the last year:

                                                                                               

At first, it looks like these images are all the same and there is no difference. But looking at the amount of yellow in the first image compared to the last one, you can see that the yellow areas are more filled in and have less green areas between them. This shows that the areas surrounded by deforestation (yellow) eventually become deforested as well and that deforestation is similar to the spreading of a disease. The most worrying part about this is that in the first year, there were very small areas of deforestation in the middle of green (forested) patches. Those were very small and may not have caused many problems in the first year, but because of the spreading of deforestation, these areas have become much larger by the last year. At this point they will cause problems such as habitat fragmentation for wildlife species. It also seems that the deforestation has spread faster between 2010 and 2020 as opposed to 2001 and 2010. This is worrisome because it means that the rate of deforestation is increasing as time goes on. I could not get any data to show up for cattle herds, but below is a graph for annual deforestation and a graph for crops for years 2001, 2010, and 2020:

As you can see, the crop rates are similar every year, but steadily increase every year. The seasonal changes are always the same, but the overall annual rate for crops increases. For deforestation, however, the seasonal changes are not always the same. 2001 and 2010 are similar in that their peak deforestation rates were at the same time, but in the year 2020, the peaks were at the times where deforestation was lowest in the past. Overall, deforestation rates for 2020 do not seem to be higher than they were in previous years. In 2001 and 2010, deforestation rates seem to be the opposite of crop rates but in 2020, deforestation rates become a bit more similar to the seasonal changes of the crop rate.

In the scenario parameters group, I changed the cattle herd expansion and crop expansion values from 0.05 to 0.5. This simulates a rapid increase in land use. Below are the output maps for years 2001, 2010, and 2020 again:

  

As you can see in these output maps, the spread of deforestation occurs at a much higher rate and by 2020, a larger amount of forested areas have become deforested because of the demand for crops and cattle herds. It is impossible to see these images as something that is not a problem. The amount of forested land left after just 20 years is alarming. Below are the graphs for the annual crops and deforestation rates:

As you can see, for the crop rates, they started out low in 2001, increased a large amount by 2010, and then dropped down to 0 by 2020. With deforestation, the highest rates were in 2001 and they continually dropped in 2010 and even more in 2020. Maybe the rates for both the crops and deforestation decrease by 2020 because there is drastically less land to use for crops and deforestation because most of it has already been converted.

 

Next is estimating the carbon losses from the two scenarios presented above. This will be done with the carbon bookkeeping model which is a very common approach to estimating carbon emissions. This model calculates annual carbon emissions by determining annual deforestation (from our previous model) and overlaying these deforested areas on a map of forest carbon biomass. This model assumes that carbon content is 50% of wood biomass and 85% of carbon stored in trees is released into the atmosphere during deforestation. This model was run for both scenario 1 and scenario 2. The graphs for the annual carbon emissions in tons for each scenario, CO2 emissions, and the graphs for annual deforestation in hectares for each scenario are below:

As you can see, these graphs all look the same. With the small (0.05) cattle herd and crop rates, the annual rates of deforestation and carbon emissions were fairly steady without much fluctuation. With the rates much higher (0.5), the rates of deforestation and carbon emissions instantly were higher and fluctuated much more. Overall, the second scenario emitted much more CO2 into the atmosphere and much more deforestation took place. Therefore, it is best if the cattle herd and crop rates remained relatively low so the CO2 emissions and deforestation rates would remain low and steady.