Presentation Type
Oral Presentation
Category
STEM (science, technology, engineering, mathematics)
Abstract/Artist Statement
Geographic distribution of plants and animals is determined by a variety of overlapping habitat factors like soil type, elevation, or canopy cover. Understanding the complicated interactions between these factors and the impacts they have on species’ distributions is increasingly important in the face of changing environments and climates. Species distribution models (SDM) measure the importance of habitat variables in determining the spatial distribution of certain species. Because SDMs include a variety of habitat variables, they can be used to answer questions about habitat suitability, predict future distributions, and inform conservation efforts.
Huckleberries (Vaccinium spp.) are a common understory shrub in the Western United States. They are culturally important to the indigenous people in the area as well as being ecologically important as a keystone species. Like all plants, huckleberries have a limited range of conditions they typically grow in, and as their habitat and abundance declines, understanding the factors that impact their distribution is vital to their conservation. Using locations across western Montana, an SDM was fit by collecting 13 biotic and abiotic habitat variables. The results of the model indicate that the most important variables for huckleberry presence were canopy cover, elevation, and precipitation.
A useful application of SDMs is predicting species distributions based on future climate scenarios. With massive shifts in climatic variables expected in the near future, habitat ranges will begin to shift to areas that meet the requirements they have for successful growth. Using the results of the original SDM, a new model was fit using climate projections for annual temperature and precipitation to determine what areas will meet the habitat requirements of huckleberries in the future. SDMs provide land managers and scientists with species distributions that can be used to inform management decisions and target critical habitats. Finally, by incorporating future climate scenarios into predictions, we can understand how organisms might respond to climate change and be better prepared for an uncertain future.
Mentor Name
Sapana Lohani
Personal Statement
When we think about the organisms around us, we rarely think about how important habitat is to their survival. Habitat variables like temperature, precipitation, soil and more come together in varying degrees to make up a habitat that either meets or falls short of the requirements for each plant. Since they have a range of requirements they can live in, understanding which variables are most important is critical to conservation. Huckleberry (Vaccinium spp.) abundance has been declining over the past decade due to a variety of environmental and climate factors. Huckleberries are dependent on many habitat variables like soil type, fire recency, and amount of precipitation. Since climate change affects each of these factors differently, determining which variables are most important determines where huckleberries will live as climate change progresses. SDMs rank the importance of certain habitat variables, resulting in predictions about what locations are, or will be, suitable habitat. For land managers, this information is imperative in protecting critical habitat and predicting shifts in their distribution to habitats that most closely represent the conditions they require for survival. While the SDM alone was valuable in determining important habitat variables, the value is in how the SDMs can be used. After fitting an SDM, it can be used with future climate predictions to determine what areas will meet the climate requirements of huckleberries today. We can better understand how their distribution will shift given changes in temperature or precipitation. It can also be used as a variable in determining other species’ distributions, like bears. If we know where huckleberries are, or where they will be, we can also determine where bears are or where they might be in the future. From a management perspective, they can be used to determine what areas should be protected or how to adapt management strategies to account for these changes. With the future of data science incorporating satellite and remote data collection methods, SDMs are well suited to include geographic data within the analysis. The data collection for this project was done using data from the USDA Soil Survey, US Forest Service, and Royal Meteorological Society, all open-source services. As satellite data becomes more accessible, the applications of SDMs will grow as will the precision. Finally, huckleberries have been part of indigenous culture for time immemorial; conserving huckleberries is vital to conserving traditional practices. The significance of this plant also extends to the ecosystem as keystone species. They provide pollen and nectar for bees, berries late in the summer for bears, and forage for deer. Huckleberries are part of a much bigger system and using SDMs can make their conservation both more informed and successful.
Predicting Huckleberry Habitat Brassfield
Predicting Huckleberry Habitat using Species Distribution Models
UC 331
Geographic distribution of plants and animals is determined by a variety of overlapping habitat factors like soil type, elevation, or canopy cover. Understanding the complicated interactions between these factors and the impacts they have on species’ distributions is increasingly important in the face of changing environments and climates. Species distribution models (SDM) measure the importance of habitat variables in determining the spatial distribution of certain species. Because SDMs include a variety of habitat variables, they can be used to answer questions about habitat suitability, predict future distributions, and inform conservation efforts.
Huckleberries (Vaccinium spp.) are a common understory shrub in the Western United States. They are culturally important to the indigenous people in the area as well as being ecologically important as a keystone species. Like all plants, huckleberries have a limited range of conditions they typically grow in, and as their habitat and abundance declines, understanding the factors that impact their distribution is vital to their conservation. Using locations across western Montana, an SDM was fit by collecting 13 biotic and abiotic habitat variables. The results of the model indicate that the most important variables for huckleberry presence were canopy cover, elevation, and precipitation.
A useful application of SDMs is predicting species distributions based on future climate scenarios. With massive shifts in climatic variables expected in the near future, habitat ranges will begin to shift to areas that meet the requirements they have for successful growth. Using the results of the original SDM, a new model was fit using climate projections for annual temperature and precipitation to determine what areas will meet the habitat requirements of huckleberries in the future. SDMs provide land managers and scientists with species distributions that can be used to inform management decisions and target critical habitats. Finally, by incorporating future climate scenarios into predictions, we can understand how organisms might respond to climate change and be better prepared for an uncertain future.