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Niwaeli’s interest in smallholder woodlots started when she was conducting her Master’s research in Tanzania, at the southern end of the East African Rift. She saw rural farmers planting pine and eucalyptus in farms that are located near a forest edge on land that had been fallow for 15 years. Her collaborators, working at another forest edge site in the Uganda portion of the Rift, were noticing a similar conversion from cropland to tree plantations. She wanted to know how much smallholder tree planting was occurring near Rift forests, and why.
Niwaeli used Google Earth to get a first impression of the distribution of woodlots. She randomly selected 60 locations in a small portion of the Rift, and digitized all the woodlots in the area. So far the team has digitized more than 4000 individual woodlots. “The really striking thing is how small these woodlots are: 93% are less than 1 hectare, and the average area is 0.45 hectares”, she said. Niwaeli extrapolated the area of woodlots she found in the digitized subset to the entire Tanzanian Southern Highlands area and estimated 80.000 ha of smallholder woodlots. That does not sound like a big number, however, it is only ~ 10.000 ha less than the amount held by the biggest tree plantation owner, the Tanzanian government. A big question remains though: Why would a subsistence farmer plant trees with a slow turnaround rate, instead of growing food crops?
In contrast to the US where farmers tend to own large and contiguous fields, in Niwaeli’s study site, farmers tend to own multiple small parcels of land that are spread throughout the landscape. Niwaeli thinks that the farmers make some allocation choices about which pieces of land will have crops, and which ones will have trees. She assumes that those parcels that are closer to the edge of the forests are not that great for growing food crops and are used for planting trees if the farmer can grow food somewhere else. “This could explain our initial field observations where we saw trees near forest edges” she added.
From the digitization, however, Niwaeli has seen that the woodlots are not limited to just forest edges, so the question of how farmers allocate land to trees is yet to be fully answered. She plans to further investigate the spatial distribution of tree plantations by mapping woodlots using Sentinel-2 satellite imagery. This will also provide a more accurate quantification of woodlot extent; and allow her to explore why some areas end up with woodlots while others do not.
style=margin-left:.25in;”>People enjoy building houses in beautiful places where they are surrounded by the beauty of nature. Unfortunately, when wildfires rage through forests, these homes are often caught in the fire’s path. As more and more people attempt to enjoy the amenities of building a home in sparsely populated areas, communities increasingly face tough decisions whether to pay for protecting these homes from wildfires that destroy property and take lives. Wildfire costs are not trivial. During the twelve-years from 1999-2011, an average of 1,354 houses were destroyed and approximately $2 billion was spent fighting wildfires, annually. Ideally, communities would have information to help predict how wildfires spread and how to minimize the number of houses lost during wildfires. Unfortunately, a lot of basic information about what happens to a community after a wildfire rips though it is unknown. Patricia Alexandre and her colleagues recently published a study that makes a first step towards describing what happens to communities across the country following wildfire events. While their results suggest that the conventional wisdom that rebuilding always happens has little support and how much is rebuilt varies across the country, they were surprised to find that new housing constructed in burned areas was happening at higher rates than rebuilding, and often at higher rates than in surrounding non-fire areas, adding complexity to the discussion.
style=”margin-left:.25in;”> To be clear, Alexandre’s research is not intended to answer whether people should rebuild following a wildfire, but to provide a snapshot of what the patterns were within affected communities across the country following recent fires. This is an important step to take to see whether patterns are consistent across a large scale and provides a dataset to begin drawing conclusions from observed rebuilding patterns. To do this work, Alexandre refined a method utilizing historical images available on Google Earth and then recruited help from students to go through and hand-digitize structures present before a fire as well as all structures that had burned, and consequently been rebuilt within five years following a fire. Nationally, the team found rebuilding rates averaged 25%, with much higher rates in the western states. For example, rates in California approached 70% of structures rebuilt following a fire. The surprising results from Alexandre’s work is first that not all burned communities are re-building within five years following a fire, and second that new buildings were constructed in burned areas at similar or even higher rates. These results indicate that communities are not just replacing homes lost to wildfires, but many are putting new homes into burned areas.
Alexandre offers multiple reasons that homeowners may build, and rebuild, in burned areas, which are inherently fire prone. One reason is that homeowners may find the value they get from living in fire-prone areas worth the fire risk, some insurance policies require rebuilding in the same spot following a fire, and many homeowners do not have the finances to relocate to an area with lower fire risk. While the reasons to build and rebuild in fire prone areas likely vary widely across the country, Alexandre’s research provides a valuable baseline to evaluate future policies or practices that communities might use to mitigate wildfire damages. Whether it’s mandating that new or rebuilt structures be constructed with safer materials, or prohibiting rebuilding in burned areas, the best way to evaluate the efficacy of these policies is to compare them to the housing patterns before and after fire events. Alexandre’s research allows that comparison to take place and hopefully inform local initiatives that could save property, money and lives.”
In an unusual twist, Paul Schilke’s interest in terrestrial birds has led him to study aquatic systems.
Many aerial insectivore bird species, such as swallows and flycatchers, have been declining since the 1980s, but researchers aren’t sure why because little is known about how these birds use the resources around them. This guild is defined by its habit of capturing flying insects in midair, as opposed to the gleaner guild that picks insects off of substrates like leaves or twigs. Many of these flying insects begin their lifecycle in aquatic systems, so Paul thought that the differential decline in the aerial feeding guild might lie in the lakes and streams.Using records from 317 locations within the Chequamegon-Nicolet National Forest in Northern Wisconsin, Paul compared presence of the aerial and gleaner insectivore guild members to estimated insect productivity in nearby lakes and streams, controlling for habitat differences (Figure 1). He estimated insect probability using a model from Bartrons et al. (2013), which used an extensive meta-analysis to determine the relationships between aquatic insect productivity and basic properties of lakes and streams such as temperature, surface area, and clarity. As expected given their feeding behavior, gleaners preferred forested habitats while aerial insectivores preferred more open areas. Interestingly, despite both guilds being insectivorous, aerial feeders demonstrated a strong preference for sites with higher insect inputs, while gleaners had no response (Figure 2).
Paul hopes that a better understanding of the food resources of aerial insectivores can lead to better conservation measures, and hopefully reverse their long term decline. He will continue his work as a PhD student in the SILVIS lab. “
In central Mexico, there is a geographically and biologically unique space featuring a mosaic of a varied diversity of flora and fauna, the Sierra Gorda Biosphere Reserve. Yet similar to other reserves in the world, human and climate drivers are changing the unique conditions of this area at an accelerated rate (Figure 2). However, this reserve remains home to important protected and endangered species that are struggling to find optimal habitat conditions elsewhere, thus it is important to identify potential areas that are suitable for keep these animal populations.Jaguar (Panthera onca) is one of these endangered species, and essential in the faunal community because of its position as a top predator (Figure 2).
Furthermore, the presence of jaguars is an indicator of the health of the ecosystems and by conserving its habitat we can also protect other species. However, is very difficult to allocate efforts to large areas and therefore we need to identify and prioritize the most important zones capable to host jaguars.In the SILVIS lab, Carlos Ramirez Reyes, created a potential species distribution model for jaguars based on presence data and using factors than can affect their distribution such as topography, landcover and precipitation among others (Figure 3).
Based on this model, he identified areas inside within and outside the reserve that could host jaguars. Additionally, by adding connectivity parameters to the models (Figure 4), he evaluated if habitat connectivity actually improves the potential distribution model for the reserve. The goal was to find patches of potential habitat that are critical for connectivity of the entire reserve.
With his research Carlos hopes to gain better knowledge for the habitat requirements of the jaguar in the region. Ultimately, the project should serve to inform nongovernmental organizations and government agencies with interests in the reserve to make decisions on how to distribute resources for the management of the species and prioritize areas that should be protected. Furthermore, this project includes a new method for obtaining potential areas through the use of landscape connectivity. Similar projects that aim to model potential species distribution can benefit from this technique given that landscape connectivity is also important for animal dispersal and gene flow in fragmented landscapes. “
Max made a technological contribution to both the fields of wildlife ecology, and parks & recreation by developing a device to measure how heavily trails are used. His goal was to quantify both group size and frequency of groups (groups/hour) along a given trail, but the available solutions were more than his research budget could manage. Having someone count hikers all day along several trails required more personnel than was practical. Meanwhile, he worried that sampling use in small time periods would provide representative data, because trail use varies throughout the day. The idea to use an automatic sensor was desirable, but the options on the market were too expensive. So he collaborated with someone with technical expertise to invent a tool that met his needs.
The solution was found in open source software and DIY hardware. First, he acquired a passive infrared (PIR) sensor that can detect warm-bodied objects that passed by (these are the same types of sensors that control automatic light switches by detecting when someone walks into a room). Then, he connected this sensor to an Arduino Uno board (http://www.arduino.cc/) that supports open source software. The board receives input from the sensor, and can be controlled by a user-written script. This is connected to a data logging shield (http://www.adafruit.com/product/1141) which contains a clock and an SD card to store data. Then, the data can be imported Excel sheet. Max used pivot tables to translate the sensor’s detections into his variables of interest. For example, the duration of time the sensor is activated can be used as an index of the number of people in a group passing by.
Max’s invention is a great alternative to what’s commercially available, in part due to the price point: one of Max’s units costs less than $250, in contrast to commercially available counters that cost about $1000/unit. Also, Max’s device can be left out in the woods for about a week between battery replacement. Its relatively small size means it can be easily hidden, which makes it relatively safe from tampering. Thus, Max continues to produce technology that will likely be used by many researchers in the future! “
Konrad Turlej, who brought his great expertise of remote sensing to SILVIS, enthusiastically started his PhD project in 2015 focusing on the mapping of tree species in Poland and in Wisconsin. The idea of the project is to map Polish and Wisconsin forests with 20-30 m resolution imagery. Konrad’s goal is ambitious: he wants to map not only where forests are, but also tree species with this medium resolution satellite data.
The reason why this is complicated is that a single tree is less than 30 mand there can be 2-4 trees species in a single pixel. Furthermore, in a satellite image, many tree species look very similar during the peak of summer. However, phenology varies greatly among tree species. Some trees have their leaves earlier, some ater. In the fall, some trees are losing leaves in September while others keep them till the first frost.
By analyzing satellite images for the entire growing season, one can analyzes so-called phenology curves. Over the course of a year, this curve looks different for different species because of phenological differences. This idea sounds quite promising but there is another challenge: Landsat, the source of 30 m imagery, provides only 1-2 images per months. In Poland, where leaves can fully come out in two weeks, this is not frequent enough to build a good phenological curve. This is why Konrad will combine Landsat data with imagery from other satellite sensors, including MODIS and Sentinel 2a.
Ultimately, the maps that Konrad is creating will be beneficial for several purposes. Describing and counting trees in the field takes a lot of time and money. Mapping maps of tree species from satellite images instead will save money and provide more timely information. With such maps, foresters can then estimate current situation on various species, amount of timber and its economic value, and eventually provide better management.
In the Altay region of Central Siberia, the forests are changing. Old-growth forest, which is needed to maintain a rich biodiversity, has been reduced drastically and now occurs mainly in protected areas. However, this important forest type is being threatened by logging, even within protected area borders, and conservationists are worried.
Although many protected areas within Altay allow some permits for logging, loggers are finding ways to cut corners and take more than their fair share. For example, some amount of selective logging might be allowed where 20% of individual trees are available for harvest, but loggers will end up removing closer to 80% of the trees. Sanitary logging, where old and dead trees can be harvested is also allowed, however, loggers often take younger, healthy trees instead. These types of logging are problematic and technically illegal, but to date have been hard to fight as finding the sites of illegal harvest is the proverbial needle in a haystack. Russian researcher Alexander Schur who visited SILIVIS in 2013 and 2014, along with SILVIS lab colleagues Matthias Bauman, and Eugenia Bragina, are at the forefront of tackling this problem, and using satellite technology to identify locations of old growth forest within protected areas that are being logged. To detect the illegal logging, they are using remotely sensed satellite imagery from Landsat in combination with forest inventory data collected by local forestry agencies. By looking at satellite images of the protected areas from different time periods, the goal is to identify where forest change had occurred, potentially identifying areas of illegal logging. The next step was to visit some of these identified disturbance areas, to check if logging had occurred or not, and this took place after Alexander returned to Altay. Overall, areas that suffered clear-cuts and high-intensity logging were well detected from the satellite imagery. In addition, areas of burnt ground were also well detected which is an important conservation concern for this region; fires in the Altay’s are often of anthropogenic origin as a byproduct of brush left behind after logging and is likewise detrimental to biodiversity. The use of these maps to detect logging is promising, because it allows for areas of forest disturbance to be easily identified, and this means that exhaustive searches by foot are no longer needed. In addition, having maps of disturbance across the entire protected areas makes it more difficult for loggers to hide their logging activities, handing the control back to the conservation practitioners and allowing to prosecute logging companies that act illegally. Forest conservationists are using the outputs of these maps to educate and raise awareness on the issues of illegal logging.
Conservation efforts are also underway, where information from forest disturbance maps are being used in conjunction with data collected on the location of rare and endangered plant and animal species that depend on old growth forest, such as Large-flowered Cypripedium orchids (Cypripedium macranthos), the Great Spotted Eagle (Aquila clanga) and the endangered black stork (Ciconia nigra). This information will then be used to designate special areas within the borders of protected areas where all logging is prohibited with the aim to protect old growth forest and subsequently biodiversity in central Siberia.”
Wildfires are a major threat to houses and people in the US. About 2 billion dollars are spent every year in preventing and suppressing fires by the US Forest Service alone, and about 1,300 houses are burned each year on average. Housing is expanding every year and the number and frequency of wildfires in increasing as well, suggesting that this problem is likely to get worse in the future. Understanding the how wildfires affect houses, and what we can do to prevent those damages, is key to guide land-use planning and management efforts in fire prone places.
“People want nice views and to live out in the country, however, houses in such locations are under high risk of wildfires”, said Patricia Alexandre, a PhD student at Silvis. Understanding the factors that explain the likelihood of a house to burn during a wildfire is of major need for land-use planning in wildfire prone areas, and for agencies such as the US Forest Service. However, there is little knowledge on this topic according to Patricia. Patricia’s research focuses on identifying the key factors that explain the likelihood of a house to burn during a fire. For this, she is using two wildfires as case studies, including a wildfire that occurred in San Diego (California) and one in Boulder (Colorado). High resolution imagery before and after the fire is being used to map all the houses within those fires, that burned due to the fires. ‘In the Boulder fire for example, we mapped about 1100 houses, and we see that 10% of them were burned after the fire’, Patricia said.
The study explores about 40 environmental variables to predict the likelihood of a house to burn, such as vegetation conditions, topography (aspects, slope, elevation, topographic position), and the spatial arrangement of house (housing density, distance to near house). According to Patricia, predicting the likely of a house to burn is a complex task. “In Colorado, the houses most likely to burn were those on high slopes or on top of ridges, as well as those located at the edges of the neighborhoods. In San Diego, however, the results were more variable. What might explain things in one fire may not work in other.”
Another important finding from this study is that the spatial arrangement of the houses matters. According to Patricia, “previous efforts used only topography and spatial arrangement to predict the likelihood of a house to burn given a wildfire, but we decided to add vegetation to see how much in fact is vegetation contributing to this phenomenon. We see that vegetation alone cannot explain why a house burned alone, the way houses are arranged on the land is also important”. Currents efforts are focused towards refining the models so to have a better understanding of the local forces that result in burned houses within a single fire. However, the ultimate goal is to expand the study to the whole US.Patricia hopes that this study will help the government to allocate resources (e.g. fuel management) in a more efficient manner, and will provide land-use planners, urban planners, and home owners with useful information and recommendations about housing construction in wildfire-prone areas. This study is a step towards Patricia’s dissertation focused on understanding the factors that explain house loss to wildlife in the US. “My ultimate goal is to develop a risk map for the whole US that tells you how likely it is that your house burns if a fire occurs”, Patricia said.
Biodiversity is important for ecosystem function and services, but it is being lost at an alarming rate often due to habitat loss or degradation. To better protect remaining biodiversity, conservation planners need a method for monitoring biodiversity over large areas. Naparat Suttidate is working on this task for her homeland of Thailand. This is fortunate as Thailand overlaps with two global biodiversity hotspots: Indo-Burma and Sundaland, and is experiencing rapid habitat loss: 53% of the country was forested in 1961, and only 33% remained forested in 2010.Naparat’s idea is to use remote sensing data to measure the biodiversity of terrestrial animals over large spatial scales. Based on MODIS data, she has calculated the Dynamic Habitat Index (DHI) for all of Thailand.
DHI is a measure of vegetative productivity derived from the fraction of absorbed photosynthetically active radiation (fPAR). The DHI summarizes three components of vegetative productivity: The first component is the cumulative annual productivity, providing an indication of overall light absorbed by vegetation. Annual productivity represents the productive capacity of a landscape across a year. If areas have high productivity, they have more energy to support diversity of species. The second component is the annual minimum productivity, providing an indication of the minimal level of vegetative cover to support organisms throughout the year. If the productivity of an area has a high minimum, it should support more biological diversity. The third component is the seasonal variation in productivity, measuring the coefficient of variation in productivity over the course of a year. If any areas have less intra-annual variability, they are more biologically diverse. Then, she correlated the DHI values with biodiversity measures based on IUCN range maps.
So far, she has found that DHI can predict the species richness of mammals, reptiles, and amphibians in Thailand. Hers is the first study to show that this type of relationship occurs in a tropical region. For now, Naparat’s study locations are randomly sampled from throughout Thailand. However, in the future she would like to focus primarily on forested and protected areas where it will have the largest environmental benefit. Her method to easily measure biodiversity will help conservation planners monitor current protected lands for changes, as well as prioritize new lands for protection, in Thailand and beyond.”