Browsing by Subject "Species distribution modeling"
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Item Global change : projecting expansion of invasive species and climate change impacts at the tree-tundra ecotone in the Himalaya(2014-08) Mainali, Kumar Prasad; Parmesan, Camille, 1961-; Singer, Micheal; White, Joseph; Young, Kenneth; Simpson, BerylModeling the distribution of species, especially of invasive species in non-native ranges, has multiple challenges. We develop some novel approaches to species distribution modeling aimed at reducing the influences of these challenges and improve realism of projections. We estimated species-environment relationship with four modeling methods, viz., random forest (RF), boosted regression trees (BRT), generalized linear models (GLM), and generalized additive models (GAM), running each of them with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges, (2) approaches of drawing background points, (3) alternate sets of predictor variables. When a species' distribution is in a non-equilibrium state, as is the case for most invasive species, model projections are very sensitive to the choice of training dataset. Contrary to previous studies, we found that model accuracy is much improved by using a global dataset for model training (both presences and background points from the world), rather than restricting data input to the species' native range. Projections outside the training region, especially in invaded regions, can be very different depending on the modeling method used. Globally projecting, we show that vast stretches of currently uninvaded geographic spaces in multiple continents harbor highly suitable habitats for Parthenium. Projections away from the sampled space (i.e. into areas of potential future invasion), can be very different with different modeling methods, raising questions about the reliability of ensemble projection. Data-driven models that efficiently fit the dominant pattern but exclude highly local features in dataset and model interactions as they appear in data (e.g., boosted regression trees) improve generalization of the species distribution modeling. Alpine treelines are responding to current climate change worldwide. To understand tree line dynamics and its potential drivers, we studied the primary two dominant tree species, Abies spectabilis (AS) and Rhododendron campanulatum (RC), on the north facing slope of two mountains in central Nepal. We determined spatial pattern of regeneration potential, mortality and abundance for various size/age classes, and we identified the most important drivers of such patterns. We also conducted a reciprocal transplant experiment on saplings of RC, moving them between species limit and treeline that were spaced apart by 150m. Young plants (<2m tall) of RC have higher density above treeline than below treeline. Mature plants (>2m tall) of RC, on the contrary, show insignificant trend towards higher density below treeline than above. Mortality of RC was always lower above treeline than below, independent of size class. AS saplings have extremely lower density above treeline than below, with mature plants being virtually absent above treeline. Elevation was identified as the only significant predictor of the decrease in density of both species above treeline. The saplings are progressively younger and shorter with distance above treeline. Both species are regenerating faster above treeline than below. These results are consistent with upward shift of the tree line of RC as a result of recent amelioration of temperature. Climatic extremes during spring affect mortality and leaf size whereas growth is affected by summer climate. Individuals from the species limit, if they survive, perform better when moved downhill than they do at home, and also out-perform the locals. Although the upper elevational boundary of RC is shifting upward, these results indicate that strong differences still exist between individuals across a short elevational gradient, with individuals at the extreme limit of the species range being more tolerant to extreme climate conditions but less tolerant of competition compared to individuals only 150m lower in elevation.Item How does uncertainty influence spatial projections of Anopheles presence in Kenya?(2016-05) Ames, Jillian Elizabeth; Miller, Jennifer A. (Jennifer Anne); Crews, Kelley A.; Busby, Joshua W.Species distribution models (SDM) are becoming a widely used framework for studying distribution and risk of vector-borne diseases, particularly as a consequence of climate change (Gonzalez et al. 2010; Porretta et al. 2013; Rochlin et al. 2013). Malaria has been one of the most extensively studied vector-borne diseases (Minakawa et al. 2005; Ryan et al. 2006; Afrane et al. 2008; Mboera et al. 2010; Nath et al. 2012), and SDM output has been used by policy makers and various aid organizations to design and implement preventative malaria programs for areas that have been identified as current or future high risk (Gething et al. 2012; Hongoh et al. 2012; Cianci et al. 2015). However, these maps and models are often developed by epidemiologists or other medical researchers and therefore issues related to representing or exploring the uncertainty in the results have often been ignored (Lindsay et al. 1998; Levine et al. 2004). Many sources of uncertainty in model outputs have been identified in SDM research, ranging from data type or measurement level (e.g., presence-only vs. presence-absence, abundance), to statistical method, to subjective decisions related to mapping the results (e.g., threshold selected to discretize continuous output). This studies employs SDM to project the spatial distribution of four species of Anopheles (malaria-carrying mosquitoes) in Kenya, focusing on the representation of uncertainty and its propagation associated with aspects of the modeling methods and the data used.Item Predictive modeling of migratory waterfowl(2011-08) Kreakie, Betty Jane; Keitt, Timothy H.; Gilbert, Lawrence; Meyers, Lauren; Singer, Michael; Yang, Zong-LiangSeveral factors have contributed to impeding the progress of migratory waterfowl spatial modeling, such as (1) waterfowl’s reliance on wetlands, (2) lack of understanding about shifts in distributions through time, and (3) large-scale seasonal migration. This doctoral dissertation provides an array of tools to address each of these concerns in order to better understand and conserve this group of species. The second chapter of this dissertation addresses issues of modeling species dependent on wetlands, a dynamic and often ephemeral habitat type. Correlation models of the relationships between climatic variables and species occurrence will not capture the full habitat constraints of waterfowl. This study introduces a novel data source that explicitly models the depth to water table, which is a simulated long-term measure of the point where climate and geological/topographic water fluxes balance. The inclusion of the depth to water table data contributes significantly to the ability to predict species probability of occurrence. Furthermore, this data source provides advantages over traditional proxies for wetland habitat, because it is not a static measure of wetland location, and is not biased by sampling method. Utilizing the long-term banding bird data again, the third chapter examines the behavior of waterfowl niche selection through time. By using the methods developed in chapter two, probability of occurrence models for the 1950s and the 1990s were developed. It was then possible to detect movements in geographic and environmental space, and how movements in these two spaces are related. This type of analysis provides insight into how different bird species might respond to environment changes and potentially improve climate change forecasts. The final chapter presents a new method for predicting the migratory movement of waterfowl. The method incorporates not only the environmental constraints of stopover habitat, but also includes likely distance and bearing traveled from a source point. This approach uses the USGS’ banding bird database; more specifically, it relies on banding locations, which have multiple recoveries within short time periods. Models made from these banding locations create a framework of migration movement, and allow for predictions to be made from locations where no banding/recovery data are available.Item Thermal ecology of the Glanville Fritillary butterfly (Melitaea cinxia)(2012-08) Advani, Nikhil Kishore; Singer, Michael C.; Parmesan, Camille; Gilbert, Lawrence; Young, Kenneth; Nice, ChristopherAnthropogenic climate warming is predicted to accelerate over the next century, with potentially dramatic consequences for wildlife. It is important to understand as well as possible how different organisms will respond to this stress. This project seeks to gain a better mechanistic understanding of the thermal biology of the Glanville Fritillary butterfly (Melitaea cinxia) at the latitudinal and elevational extremes of its range. Investigation of the temperatures at which adult butterflies took spontaneous flight revealed a significant difference between populations from the elevational extremes, with insects from high elevation taking flight at lower thoracic temperatures than those from low elevation. Contrary to expectation, there was no systematic effect of latitude on takeoff temperature. If these measures represent adaptation to climate, then these effects are not simple and the influences of elevation and latitude are not the same. Investigation of thermal tolerance across all life cycle stages found no difference in larval performance between the populations tested. There was however an effect of treatment. This suggests that in M. cinxia, even populations from different extremes of the range may not differ in their thermal tolerance. The effect of treatment suggests that there is temperature-induced plasticity. The adaptive significance of this has been explored to some extent. Investigation of heat shock protein expression between the latitudinal extremes finds no difference in Hsp21.4 expression when exposed to heat stress, however both Hsp20.4 and Hsp90 were upregulated in response to heat stress. For Hsp20.4, there were significant differences in expression between the populations. Finally, a species distribution model using maximum entropy techniques was conducted for M. cinxia, predicting both the current and future (2100) distributions of the species. The model closely matches the known current distribution, and predicts a clear northward range shift in response to climate change.