abstract: Species distribution modeling is used to study changes in biodiversity and species range shifts, two currently well-known manifestations of climate change. The focus of this study is to explore how distributions of suitable habitat might shift under climate change for shrub communities within the Santa Monica Mountains National Recreation Area (SMMNRA), through a comparison of community level to individual species level distribution modeling. Species level modeling is more commonly utilized, in part because community level modeling requires detailed community composition data that are not always available. However, community level modeling may better detect patterns in biodiversity. To examine the projected impact on suitable habitat in the study area, I used the MaxEnt modeling algorithm to create and evaluate species distribution models with presence only data for two future climate models at community and individual species levels. I contrasted the outcomes as a method to describe uncertainty in projected models. To derive a range of sensitivity outcomes I extracted probability frequency distributions for suitable habitat from raster grids for communities modeled directly as species groups and contrasted those with communities assembled from intersected individual species models. The intersected species models were more sensitive to climate change relative to the grouped community models. Suitable habitat in SMMNRA's bounds was projected to decline from about 30-90% for the intersected models and about 20-80% for the grouped models from its current state. Models generally captured floristic distinction between community types as drought tolerance. Overall the impact on drought tolerant communities, growing in hotter, drier habitat such as Coastal Sage Scrub, was predicted to be less than on communities growing in cooler, moister more interior habitat, such as some chaparral types. Of the two future climate change models, the wetter model projected less impact for most communities. These results help define risk exposure for communities and species in this conservation area and could be used by managers to focus vegetation monitoring tasks to detect early response to climate change. Increasingly hot and dry conditions could motivate opportunistic restoration projects for Coastal Sage Scrub, a threatened vegetation type in Southern California. / Dissertation/Thesis / M.A. Geography 2014

Great Basin bristlecone pine (Pinus longaeva D.K. Bailey) is a keystone species of the subalpine forest in the Great Basin and western Colorado Plateau ecoregions in Utah, Nevada, and California. Bristlecone pine is also the world's longest-lived non-clonal organism, with individuals occasionally reaching ages up to 5,000 years old. Because of its longevity, bristlecone pine contains an important proxy record of climate data in its growth rings. Despite its ecological and scientific importance, bristlecone pine's distribution and associated environmental drivers are poorly understood. Geospatial technologies, including unmanned aircraft systems (UAS), remote sensing, geographic information systems (GIS), and spatial modeling techniques can be used to quantify and characterize biotic and abiotic factors that constrain the fundamental and realized niches of bristlecone pine and other subalpine forest species. In Chapter 1, we describe workflows and important technical and logistical considerations for collecting aerial imagery in mountainous areas using small UAS, enabling high-quality remotely sensed datasets to be assembled to study the ecology of subalpine forests. In Chapter 2, we discuss a unique outlier population of bristlecone pine found in the Stansbury Mountains, Utah. We used GIS to delineate boundaries for five small stands of bristlecone pine and examined two competing hypotheses that could explain the species' presence in the range: 1) that the current population is a relict from the Pleistocene, or 2) that long-distance dispersal mechanisms led to bristlecone pine's migration from other mountain ranges during or after the warming period of the Pleistocene/Holocene transition. Potential migration routes and barriers to migration were considered in our effort to understand the dynamics behind the presence of this unique disjunct population of bristlecone pine. Chapter 3 describes a comprehensive mapping effort for bristlecone pine across its entire distribution. Using data from historic maps, vegetation surveys, herbarium records, and an online ecological database, we compiled nearly 500 individual map polygons in a public-facing online GIS database representing locations where bristlecone pine occurs. Using these occurrence data, we modeled the suitable habitat of the species with Maximum Entropy (MaxEnt), examining the relative importance of 60 environmental variables in constraining the species distribution. A probability map was generated for bristlecone pine, and the environmental variables were ranked in order of their predictive power in explaining the species distribution. We found that January mean dewpoint temperature and February precipitation explained over 80% of the species distribution according to the MaxEnt model, suggesting that the species favors drier air conditions and increased snowfall during winter months. These three studies demonstrate that geospatial tools can be effectively used to quantify and characterize the habitat of bristlecone pine, leading to improved management and conservation of the species in the face of multiple threats, including mountain pine beetle (MPB), white pine blister rust (WPBR), and possible habitat constriction due to climate change.

En komplett bild av bosättningar från förhistorisk tid har alltid varit svår att kartlägga med tanke på hur tiden gömt undan dessa platser och lämningar genom nedbrytning av det material de tillverkats av och uppbyggnaden av nya lager av sediment. Arkeologer har genom tiden använt sig av en mängd olika typer av metoder och tekniker för att finna spår av dessa förhistoriska lämningar. I modern tid har GIS blivit ett vanligt användningsområde till att assistera den här processen. I den här studien är det ”predictive modeling” som använts för att förutsäga sannolikheten av att kunna hitta nya arkeologiska fynd baserat på redan funna och dess samband med egenskaper i landskapet och miljön. Med en relativt ny metod som använder sig av principen för maximal entropi i sin algoritm hoppas den här studien kunna visa prov på potentialen för den här tekniken i Sverige till att underlätta arkeologers arbete samt ge en inblick i det förflutna när det gäller människors framgång och val av bosättning. Genom att skapa modeller med programvaran Maxent producerades sannolikhetskartor över studieområdet baserat på 221 fyndplatser och upp till 16 faktorer samt statistiska diagram för att ge en djupare inblick i modellens byggnadsprocess. Validering av resultatet visade prov på mycket stor framgång. Trots det utmärkta resultatet finns en viss skepsis i hur behjälplig just den här modellen vore för arkeologin i att hitta nya bosättningar från forntiden. I och med att den här studien är rätt begränsad i sin tillgång till data har den ändå visat potentialen i hur algoritmer med användning av principen för maximal entropi har för arkeologin inom Sverige. Med ett större och mer precisare urval av fyndplatser och faktorer, både över miljö, landskap och övrigt, har modeller som denna en stor potential till att både assistera arkeologin att hitta fortfarande gömda fornnordiska boplatser och utvinna information om forntida människors liv och samhällen.

Background: The mosquito Aedes aegypti has long been a vector for human illness in the Southeastern United States. In the past, it has been responsible for outbreaks of dengue, chikungunya, and yellow fever and, very recently, the Zika virus that has been introduced to the region. Multiple studies have modeled the geographic distribution of Ae. aegypti as a function of climate factors; however, this ignores the importance of humans to the anthropophilic biter. Furthermore, Ae. aegypti thrives in areas where humans have created standing water sites, such as water storage containers and trash. As models are developed to examine the potential impact of climate change, it becomes increasingly important to include the most comprehensive set of predictors possible. Results: This study uses Maxent, a species distribution model, to evaluate the effects of adding poverty and population density to climate-only models. Performance was evaluated through model fit statistics, such as AUC, omission, and commission, as well as individual variable contributions and response curves. Models which included both population density and poverty exhibited better predictive power and produced more precise distribution maps. Furthermore, the two human population characteristics accounted for much of the model contribution-more so than climate variables. Conclusions: Modeling mosquito distributions without accounting for their dependence on local human populations may miss factors that are very important to niche realization and subsequent risk of infection for humans. Further research is needed to determine if additional human characteristics should be evaluated for model inclusion.

Background: As the climate warms, many species of reptiles are at risk of habitat loss and ultimately extinction. Locations of suitable habitat in the past, present, and future were modeled for several lizard species using MaxEnt, incorporating climatic variables related to temperature and precipitation. In this study, we predict where there is currently suitable habitat for the genus Pogona and potential shifts in habitat suitability in the past and future. Methods: Georeferenced occurrence records were obtained from the Global Biodiversity Information Facility, climate variables (describing temperature and precipitation) were obtained from WorldClim, and a vegetation index was obtained from AVHRR satellite data. Matching climate variables were downloaded for three different past time periods (mid-Holocene, Last Glacial Maximum, and Last Interglacial) and two different future projections representative concentration pathways (RCPs 2.6 and 8.5). MaxEnt produced accuracy metrics, response curves, and probability surfaces. For each species, parameters were adjusted for the best possible output that was biologically informative. Results: Model results predicted that in the past, there was little suitable habitat for P. henrylawsoni and P. microlepidota within the areas of their current range. Past areas of suitable habitat for P. barbata were predicted to be similar to the current prediction. Pogona minor and P. nullarbor were predicted to have had a more expansive range of suitable habitat in the past, which has reduced over time. P. vitticeps was predicted to have less suitable habitat in the past when examining the region of their known occurrence; however, there was predicted growth in suitable habitat in Western Australia. Both 2070 models predict a similar distribution of habitat; however, the model produced using the 2070 RCP 8.5 climate change projection showed a larger change, both in areas of suitable habitat gain and loss. In the future, P. henrylawsoni and P. microlepidota might gain suitable habitat, while the other four species could possibly suffer habitat loss. Discussion: Based on the model results, P. henrylawsoni and P. microlepidota had minimal areas of suitable habitat during the Last Glacial Maximum, possibly due to changes in tolerance or data/model limitations, especially since genetic analyses for these species suggest a much earlier emergence. The predicted late Quaternary habitat results for all species of Pogona are conservative and should be compared to the fossil record which is not possible at the moment due to the current inability to identify fossil Pogona to the species level. P. nullarbor and P. vitticeps future models predict substantial habitat loss. P. nullarbor could potentially be considered vulnerable in the present since it already has a restricted range, and a conservation plan may need to be considered.

Few studies utilise modern species distribution data and modeling to make predictions for examining potential fossil localities. Instead, species distribution modeling is often used for palaeoenvironmental interpretations. Using palaeoclimate data to model potential past distributions for a species provides a prediction showing areas where its fossil remains may be found. In this study, the current, Last Glacial Maximum, and Last Interglacial potential distributions of the arid steppe-obligate saiga antelope (Artiodactyla: Bovidae, Saiga tatarica) were modeled using the species distribution model Maxent. Few fossil records exist, but available fossil locality records were used to validate both palaeo models, resulting in speculative predictions about where the saiga may have lived. Known fossil localities of saiga from the Last Glacial Maximum time period were located within predicted moderately suitable environments, while four of seven Last Interglacial fossil localities were located within predicted moderately suitable environments, suggesting that models can accurately identify areas where fossils for the saiga can be found. Specifically, these models suggest saiga fossils may be located in northwestern and northeastern China, the western and central regions of the Middle East, and southern Alaska. The predicted areas in northeastern China are of particular interest because saiga fossils have not been identified in this region, but some palaeontologists theorize that northeast China may have been suitable for saiga in the past. The models lend credence to this argument.

While range-wide population declines have prompted extensive research on greater sage-grouse (Centrocercus urophasianus), basic information about southern periphery populations, such as the Bald Hills population in southern Utah, has not been documented. The objective of this research was to determine habitat preferences and space use patterns of the Bald Hills sage-grouse population which occurs in an area of high potential for renewable energy development. I tracked 66 birds via VHF telemetry in 2011 and 2012 and surveyed vegetation plots throughout the study area. I found that the population was primarily one-stage migratory with seasonal distributions that did not correspond well with previously developed suitable habitat maps (based on local biologist knowledge and lek data) for all seasons; I also found that mean home range sizes ranged from 82 km2 to 157 km2. Nesting hens did not select for any measured vegetation characteristics within the study area, while brood-rearing hens selected for high forb cover. Birds at summer sites (non-reproductive bird locations during the summer season) selected for greater grass and forb cover and lower shrub cover compared with random sites. Overall, Bald Hills sage-grouse used areas with greater shrub canopy cover and lower grass and forb cover than recommended in habitat guidelines. Ten predictor variables were used to model suitable seasonal habitat using Maximum Entropy (maxent). All models were created for the Bald Hills population and projected to the Bureau of Land Management Cedar City Field Office management area and produced excellent model fit (AUC > 0.900). The Bald Hills population had similar nesting and winter habitat preferences as other populations but different brood-rearing and summer habitat preferences. I found local management techniques to be an important driver of seasonal habitat selection; birds selected for areas that had undergone habitat treatments (such as broadcast burn and crushing) within the previous 10 years. My results indicated the Bald Hills periphery population occupies marginal habitat and has adapted unique seasonal habitat preferences. Managers of isolated, fringe, and low-density populations should develop locally specific management guidelines to address the unique adaptations and ensure the persistence of these populations.

No description available.

Modeling habitat suitability is beneficial for management and conservation of a species. Although data-rich models are commonly used, opinion-based models may be a beneficial alternative to estimate suitable habitat locations. Despite the increasing use of habitat models, few studies have linked habitat model covariates (i.e., land cover, weather, and normalized difference vegetation indexes (NDVI)) to demographic parameters. This study evaluates model performance and transferability of maximum entropy (MaxEnt) and expert opinion models for predicting American beaver (Castor canadensis) distribution in the southeastern US. I also investigated the relationship of environmental and habitat model covariates to beaver survival. The model’s predictive performance and transferability were evaluated using the area under the curve (AUC) index. Both model approaches performed well at predicting beaver presence. While MaxEnt had better performance, the expert models predicted greater areas as suitable for beaver. Beaver survival was estimated for northern Alabama and was found to be influenced by NDVI and weather covariates in this study.

Watershed systems are experiencing rapid changes to water quality and hydrologic regimes due in part to climate-induced changes in temperature and precipitation, urbanization, and increases in aquatic invasive species. Aquatic invasive species are one of the primary threats to ecosystems, contributing to loss of biodiversity, altered hydrologic regimes, and stream degradation. Urban land use and climatic factors influence the spread of invasive species, presenting greater challenges for future invasive species management. There is a need for more research that evaluates the watershed process in connection with urban land use and climate change factors in relation to invasive species spread. This study will examine factors of climate change and land use that may be influencing the spread and occurrence of aquatic invasive plants within the Connecticut River watershed. There will be four species involved in this study: Eurasian milfoil (Myriophyllum spicatum), Variable milfoil (Myriophyllum heterophyllum), Hydrilla (Hydrilla verticillate), and the European water chestnut (Trapa natans). Hydrological conditions within the watershed will be analyzed using the SWAT model through the HAWQS interface. ArcGIS Pro will be used to combine and prepare data so that it may be utilized through MaxEnt. MaxEnt will be used to create species distribution models to estimate the probability of the presence of invasive aquatic plant species in the Connecticut river watershed.