Improving Species Distribution Models with Bias Correction and Geographically Weighted Regression: Tests of Virtual Species and Past and Present Distributions in North American Deserts

abstract: This work investigates the effects of non-random sampling on our understanding of species distributions and their niches. In its most general form, bias is systematic error that can obscure interpretation of analytical results by skewing samples away from the average condition of the system they represent. Here I use species distribution modelling (SDM), virtual species, and multiscale geographically weighted regression (MGWR) to explore how sampling bias can alter our perception of broad patterns of biodiversity by distorting spatial predictions of habitat, a key characteristic in biogeographic studies. I use three separate case studies to explore: 1) How methods to account for sampling bias in species distribution modeling may alter estimates of species distributions and species-environment relationships, 2) How accounting for sampling bias in fossil data may change our understanding of paleo-distributions and interpretation of niche stability through time (i.e. niche conservation), and 3) How a novel use of MGWR can account for environmental sampling bias to reveal landscape patterns of local niche differences among proximal, but non-overlapping sister taxa. Broadly, my work shows that sampling bias present in commonly used federated global biodiversity observations is more than enough to degrade model performance of spatial predictions and niche characteristics. Measures commonly used to account for this bias can negate much loss, but only in certain conditions, and did not improve the ability to correctly identify explanatory variables or recreate species-environment relationships. Paleo-distributions calibrated on biased fossil records were improved with the use of a novel method to directly estimate the biased sampling distribution, which can be generalized to finer time slices for further paleontological studies. Finally, I show how a novel coupling of SDM and MGWR can illuminate local differences in niche separation that more closely match landscape genotypic variability in the two North American desert tortoise species than does their current taxonomic delineation.Dissertation/ThesisDoctoral Dissertation Geography 201

Modelling plant trait variability in changing arid environments

Modellierung der Variabilität von Pflanzen-Traits auf Populations- und Lebensgemeinschaftsebene in ariden Gebieten mit Umweltveränderungen. Lebensgemeinschaften in ariden Gebieten sind angesichts globaler Umweltveränderungen besonders anfällig, da sie höchst unvorhersagbaren Umweltbedingungen ausgesetzt sind. Das Schicksal von Gemeinschaften in einer ungewissen Zukunft kann durch das Verständnis der Triebkräfte dieser Gemeinschaften aufgeklärt werden. Das Zusammenspiel der Triebkräfte der Gemeinschaften kann mit Hilfe von Ansätzen entschlüsselt werden, die auf funktionalen Merkmalen (Traits) basieren, weil sie Pflanzenstrategien und die Reaktionen der Gemeinschaften auf Umweltveränderungen beschreiben können. Darüber hinaus liefert die inter- und intraspezifische Variabilität der Traits die notwendigen Anhaltspunkte für die Identifizierung von Überlebensstrategien von Wüstenpflanzen unter wechselhaften Umweltbedingungen. Die Erforschung von Wüstenpflanzengemeinschaften könnte jedoch aufgrund der räumlichen und zeitlichen Heterogenität der ariden Umweltbedingungen eine Herausforderung darstellen. Modellierungsansätze unterstützen und ergänzen empirische, trait-basierte Ansätze bei der Erforschung von Wüstenpflanzengemeinschaften und ihrer Triebkräfte und Dynamik in sich verändernden ariden Gebieten. Das Gesamtziel dieser Arbeit war es, die intra- und interspezifische Variabilität der funktionalen Traits in ariden Umgebungen zu erforschen und zu untersuchen, wie sich diese Variabilität auf die Fähigkeit von Pflanzen auswirkt, Trockenstress zu tolerieren und in der Konkurrenz mit ihren Nachbarn erfolgreich zu sein. Um dieses Ziel zu erreichen, habe ich ein räumlich-explizites individuen- und trait-basiertes Simulationsmodell entwickelt, implementiert und analysiert, ein Simulationsexperiment durchgeführt, Daten aus empirischen Experimenten analysiert und einen Überblick der Literatur zu trait-basierten Modellen und Metamodellierungsansätzen zusammengestellt. Meine Forschung basiert auf Daten zu annuellen Pflanzengemeinschaften in der Wüste Negev in Israel, die von der Echte Rose von Jericho (Anastatica hierochuntica) dominiert werden. Die Literaturzusammenschau in Kapitel 1 offenbart, dass trait-basierte Modelle eine geeignete Methode sind, um Veränderungen in den Mustern von Gemeinschaften unter globalen Veränderungen vorherzusagen und die zugrunde liegenden Mechanismen der Zusammensetzung und Dynamik von Lebensgemeinschaften zu verstehen. Durch die Kombination von Modellierung und trait-basierten Ansätzen lassen sich technische Herausforderungen, Skalierungsprobleme und Datenknappheit überwinden. Insbesondere wurde eine Kombination aus trait-basierten Ansätzen und individuenbasierter Modellierung empfohlen, um die Parametrisierung der Modelle zu vereinfachen, Interaktionen zwischen Pflanzen auf individueller Ebene zu erfassen und die Gemeinschaftsdynamik zu erklären. Eine Forderung aus Kapitel 1 umsetzend wurde in Kapitel 2 das räumlich-explizite, trait- und individuenbasierte ATID-Modell entwickelt, implementiert und analysiert, um zu untersuchen, wie Gemeinschaftsdynamiken aus Pflanzentraits und Interaktionen von Pflanzen untereinander und mit ihrer Umwelt entstehen. Die Sensitivitätsanalyse des Modells hob die funktionalen Traits von Pflanzen als Schlüsselfaktoren der Gemeinschaftsdynamik hervor, wobei den Umweltfaktoren im Modell eine relativ geringere Bedeutung zugewiesen wurde. Die sensitivitätverursachenden Traits umfassten sowohl solche Traits, die an den Pflanze-Pflanze-Interaktionen beteiligt waren, wie zum Beispiel die relative Wachstumsrate und maximale Biomasse, als auch solche, die die Toleranz gegenüber abiotischem Stress fördern, wie die Keimruhe und Keimungswahrscheinlichkeit. Unter den Umweltfaktoren waren die Verfügbarkeit von Bodenwasser und Niederschlag die einflussreichsten Faktoren. Die besondere Rolle von funktionalen Traits in der Gemeinschaftsdynamik einjähriger Wüstenpflanzen zeigt die Bedeutung trait-basierter Strategien als Anpassung an die harschen Bedingungen in ariden Gebieten. Kapitel 3 befasst sich mit den Ergebnissen eines Simulationsexperiments, das mit dem ATID-Modell durchgeführt wurde. Dieses Experiment untersuchte den Einfluss funktionaler Traits auf die Gemeinschaftsdynamik, die bei zwei Überlebensstrategien eine Rolle spielen, die in der Studie in einem neuen Strategiekonzept als "Schutz-Konkurrenz"- und "Flucht-Kolonisierungs"-Strategien definiert wurden. Diese Strategien unterschieden sich nicht nur in der Samengröße und der Anzahl der Samen, sondern auch in bestimmten Pflanzentraits, die mit Konkurrenz und Überleben zusammenhängen und die in der Sensitivitätsanalyse des Modells aus Kapitel 2 hervorgehoben worden waren. Die Integration der Konzepte des Kolonisierung-Konkurrenz-Trade-offs und des Entkommens in Zeit und Raum in einem neuen Strategiekonzept ergab eine realistischere Darstellung der Arten, da die integrierten Strategien den gesamten Lebenszyklus der Pflanze berücksichtigen. Um ein besseres Verständnis empirischer Trait-Verteilungen zu erlangen, wurden in Kapitel 4 Daten zur intraspezifischen Traitvariabilität und zu Trait-Räumen der annuellen Wüstenpflanze A. hierochutica aus einem Gewächshausversuch analysiert. Hohe Salzkonzentrationen hatten signifikante Auswirkungen auf die Durchschnittswerte der funktionalen Traits der Pflanzen. Zusätzlich beeinflusste Salzstress die intraspezifischen Trait-Räume unterschiedlich in Bezug auf die Umweltbedingungen des Ursprungsortes der Pflanzen. Die Trait-Räume der Populationen, die vom gleichen Standort stammten, aber unterschiedlichen Salzstress-Niveaus ausgesetzt waren, wurden mit zunehmender Aridität unähnlicher. Daher erwiesen sich die intraspezifische Trait-Variabilität und die Salzeffekte als wesentlich für die Aufdeckung von Prozessen auf Populations- und Lebensgemeinschaftsebene in Wüsten und sollten in zukünftigen Versionen des ATID-Modells berücksichtigt werden. Zur Unterstützung der zukünftigen Entwicklung des in Kapitel 2 entwickelten ATID-Modells wurden in Kapitel 5 Metamodelltypen und ihre Anwendungsbereiche in der individuenbasierten Modellierung überprüft und bewertet. Die Überprüfung berücksichtigte 40 Metamodelle, die für die Sensitivitätsanalyse, Kalibrierung, Vorhersage und Skalierung von individuenbasierten Modellen eingesetzt werden können und als Leitfaden für die Implementierung und Validierung von Metamodellen dienen können. Insgesamt beleuchtet diese Arbeit und insbesondere die Analysen des ATID-Modells, wie trait-basierte Modellierungsansätze zum Verständnis des Zusammenspiels der Schlüsseltriebkräfte von Wüstenpflanzengemeinschaften in ariden Umgebungen beitragen können. Die begleitende Analyse des Gewächshausexperiments und die kritischen Literaturübersichten dienen als Grundlage für zukünftige Erweiterungen des Modells und die in dieser Arbeit identifizierten Wege zur Überwindung technischer Herausforderungen und Datenknappheit. Darüber hinaus empfiehlt diese Dissertation eine intensivere Untersuchung der Strategien annueller Wüstenpflanzen für das Überleben unter zeitlich und räumlich heterogenen Umweltbedingungen mit besonderem Schwerpunkt auf funktionalen Pflanzen-Traits. Somit bietet das in dieser Arbeit vorgestellte Grundmodell die Basis für zukünftige Forschungen über das Schicksal von Lebensgemeinschaften in ariden Gebieten unter dem Einfluss globaler Umweltveränderungen.Communities in arid environments are especially vulnerable to global change because they experience highly unpredictable environmental conditions. The fate of communities in an uncertain future may be elucidated by understanding the drivers of these communities. The interplay between community drivers may be unravelled by using approaches based on functional traits because traits describe plant strategies and the responses of communities to environmental changes. Furthermore, inter- and intraspecific trait variability provides the necessary cues to identify survival strategies of desert plants under fluctuating environmental conditions. However, studying desert plant communities is challenging due to the spatial and temporal heterogeneity of arid environments. Modelling approaches support and complement empirical trait-based approaches in exploring desert plant communities and their drivers and dynamics in changing arid environments. The overarching aim of this thesis was to explore intra- and inter-specific variability of functional traits in arid environments and to investigate how this variability affects the ability of plants to tolerate aridity stress and succeed in competition with their neighbours. To address this aim, I developed, implemented and analysed a spatially explicit individual- and trait-based simulation model, conducted a simulation experiment, analysed data from model simulations and empirical experiments and synthesized the literature on trait-based models and metamodelling approaches. My research was focused on annual plant communities dominated by the True Rose of Jericho (Anastatica hierochuntica L.) in the Negev desert in Israel. According to the review in chapter 1, trait-based models are a suitable method to predict changes in community patterns under global change and to understand the underlying mechanisms of community assembly and dynamics. Combining modelling and trait-based approaches overcomes technical challenges, scaling problems, and data scarcity. Specifically, a combination of trait-based approaches and individual-based modelling was recommended to simplify the parameterization of models and to capture plant-plant interactions at the individual level, and to explain community dynamics. In chapter 2, in line with the major claim of chapter 1, the spatially explicit trait- and individual-based ATID-model was developed, implemented and analysed to explore how community dynamics arise from plant traits and the interactions among plants and with their environment. The sensitivity analysis of the model highlighted plant functional traits as key drivers of community dynamics and indicated that environmental factors were less important in the model. The outlined traits included both those traits that are involved in plant-plant interactions, such as relative growth rate and maximum biomass, and those that promote tolerance to abiotic stress, such as dormancy and germination probability. Among the environmental factors, the most influential factors were soil water availability and precipitation. The special role of functional traits in the community dynamics of desert annual plants indicates the importance of trait-based strategies as an adaptation to the stressful arid environment. Chapter 3 addresses the results from a simulation experiment that was conducted in the ATID-model. This experiment explored the influence of functional traits involved in two survival strategies defined in the study as ‘protective-competition’ and ‘escape-colonization’ strategies on community dynamics. These strategies differed not only in seed size and the number of seeds, but also in the plant functional traits related to competition and survival, which were highlighted in the sensitivity analysis of the model from chapter 2. Merging the colonization-competition trade-off with escape in time and space into one strategy set provided a more realistic representation of species because the merged strategies related to the entire plant life cycle. To gain more understanding on empirical trait distributions, in chapter 4 data on intraspecific trait variability and trait spaces of the desert annual plant A. hierochutica from a nethouse experiment were analysed. High salinity had significant effects on the average values of plant functional traits. Additionally, salinity stress affected the intraspecific trait spaces differentially with respect to the environmental conditions of the site of origin. Trait spaces of the populations originating from the same site but exposed to different salt stress levels became more dissimilar with increasing environmental aridity. Thus, intraspecific trait variability and salinity effects turned out to be essential in revealing population- and community-level processes in deserts and should be considered in future versions of the ATID-model. In support of the future development of the ATID-model developed in chapter 2, common metamodel types and the purposes of their usage for individual-based models were reviewed and evaluated in chapter 5. The review considered 40 metamodels applied for sensitivity analysis, calibration, prediction and scaling-up of individual-based models and can be used as a guide for the implementation and validation of metamodels. Overall, this thesis, and particularly the ATID-model analyses, highlights how trait-based modelling approaches can contribute to understanding the interplay between key drivers of desert plant communities in arid environments. The accompanying analysis of the nethouse experiment and critical literature reviews outline future extensions of the model and the ways to overcome the technical challenges and data scarcity identified in this thesis. Moreover, this thesis advocates for more intensive studies of the strategies of desert annual plants to survive in temporally and spatially heterogeneous environments with a focus on plant functional traits. Thus, the modelling framework presented in this thesis provides the basis for future research on the fate of communities in arid environments under global change

State of the Art on Artificial Intelligence in Land Use Simulation

[Abstract] This review presents a state of the art in artificial intelligence applied to urban planning and particularly to land-use predictions. In this review, different articles after the year 2016 are analyzed mostly focusing on those that are not mentioned in earlier publications. Most of the articles analyzed used a combination of Markov chains and cellular automata to predict the growth of urban areas and metropolitan regions. We noticed that most of these simulations were applied in various areas of China. An analysis of the publication of articles in the area over time is included.This project was supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (ref. ED431G/01 and ED431D 2017/16), the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002 and UNLC13-13-3503), and the European Regional Development Funds (FEDER). CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia,” supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaria Xeral de Universidades” (grant no. ED431G 2019/01)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G 2019/0

EXPLORING SIGNALS OF HISTORICAL DEMOGRAPHY IN BOREAL MAMMALS THROUGH INTEGRATION OF STATISTICAL CONSERVATION PHYLOGENETICS, TAXONOMY, AND COMPARATIVE PHYLOGEOGRAPHY

Understanding how diversity is partitioned across the landscape can provide perspectives related to the environmental processes that have influenced the evolutionary history of organisms. This main idea, often termed phylogeography, serves as the backdrop to my research where I explore three broad concepts including historical biogeography, cryptic diversity and ecology, and conservation phylogenetics. I address various questions in each of these concepts by using a set of mammals that are associated with montane and mesic environments of North America. More specifically, I focus on the jumping mice (Zapodidae) to test hypotheses that scale to the broader community. This approach allows for a more refined understanding and interpretation of how species have responded to geophysical changes of the past that may be useful for predicting how future environmental pressures may influence geographically oriented lineages. By integrating across multiple disciplines of population genetics, phylogenetics, phylogeography, distribution modeling, and paleoclimatology, I assess how environmental change has left an imprint on the genetics and ecology of various organisms. Signatures of the past are useful to forecast conservation issues of the future

Data-Driven Framework for Understanding & Modeling Ride-Sourcing Transportation Systems

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for all trips made within the city since November 1, 2018. The data comprises the trip ends (pick-up and drop-off locations), trip timestamps, trip length and duration, fare including tipping amounts, and whether the trip was authorized to be shared (pooled) with another passenger or not. Therefore, the main goal of this dissertation is to develop a comprehensive data-driven framework to understand and model the system using this data from Chicago, in a reproducible and transferable fashion. Using data fusion approach, sociodemographic, economic, parking supply, transit availability and accessibility, built environment and crime data are collected from open sources to develop this framework. The framework is predicated on three pillars of analytics: (1) explorative and descriptive analytics, (2) diagnostic analytics, and (3) predictive analytics. The dissertation research framework also provides a guide on the key spatial and behavioral explanatory variables shaping the utility of the mode, driving the demand, and governing the interdependencies between the demand’s willingness to share and surge price. Thus, the key findings can be readily challenged, verified, and utilized in different geographies. In the explorative and descriptive analytics, the ride-sourcing system’s spatial and temporal dimensions of the system are analyzed to achieve two objectives: (1) explore, reveal, and assess the significance of spatial effects, i.e., spatial dependence and heterogeneity, in the system behavior, and (2) develop a behavioral market segmentation and trend mining of the willingness to share. This is linked to the diagnostic analytics layer, as the revealed spatial effects motivates the adoption of spatial econometric models to analytically identify the ride-sourcing system determinants. Multiple linear regression (MLR) is used as a benchmark model against spatial error model (SEM), spatially lagged X (SLX) model, and geographically weighted regression (GWR) model. Two innovative modeling constructs are introduced deal with the ride-sourcing system’s spatial effects and multicollinearity: (1) Calibrated Spatially Lagged X Ridge Model (CSLXR) and Calibrated Geographically Weighted Ridge Regression (CGWRR) in the diagnostic analytics layer. The identified determinants in the diagnostic analytics layer are then fed into the predictive analytics one to develop an interpretable machine learning (ML) modeling framework. The system’s annual average weekday origin-destination (AAWD OD) flow is modeled using the following state-of-the-art ML models: (1) Multilayer Perceptron (MLP) Regression, (2) Support Vector Machines Regression (SVR), and (3) Tree-based ensemble learning methods, i.e., Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost). The innovative modeling construct of CGWRR developed in the diagnostic analytics is then validated in a predictive context and is found to outperform the state-of-the-art ML models in terms of testing score of 0.914, in comparison to 0.906 for XGBoost, 0.84 for RFR, 0.89 for SVR, and 0.86 for MLP. The CGWRR exhibits outperformance as well in terms of the root mean squared error (RMSE) and mean average error (MAE). The findings of this dissertation partially bridge the gap between the practice and the research on ride-sourcing transportation systems understanding and integration. The empirical findings made in the descriptive and explorative analytics can be further utilized by regional agencies to fill practice and policymaking gaps on regulating ride-sourcing services using corridor or cordon toll, optimally allocating standing areas to minimize deadheading, especially during off-peak periods, and promoting the ride-share willingness in disadvantage communities. The CGWRR provides a reliable modeling and simulation tool to researchers and practitioners to integrate the ride-sourcing system in multimodal transportation modeling frameworks, simulation testbed for testing long-range impacts of policies on ride-sourcing, like improved transit supply, congestions pricing, or increased parking rates, and to plan ahead for similar futuristic transportation modes, like the shared autonomous vehicles

Understanding the Problem Structure of Optimisation Problems in Water Resources

Optimisation algorithms are widely used in water resources to identify the optimal solutions for problems with multiple possible solutions. Many studies in this field focus on the development and application of advanced optimisation algorithms, making significant contributions in improving optimisation performance. On the other hand, the performance of optimisation algorithms is also related to the features of the problems being solved, therefore, selecting appropriate algorithms for corresponding problems is also a key to the success of optimisation. Although a number of metrics have been developed to assess these features, they have not been applied to problems in the water resources field. The primary reason for this is that the computational cost associated with the calculation of many of these metrics increases significantly with problem size, making them unsuitable for problems in water resources. Consequently, there is a lack of knowledge about the features of problems in the water resources field. This PhD thesis aims to understand the features of problems in water resources, and the process can be split into two stages. The first stage is to identify metrics that can be applied within an affordable computational cost. This is addressed in the first content chapter (Paper 1). The second stage is to apply metrics identified in the first stage to understand the features of problems in the water resources field, including the calibration of artificial neural network models (Paper 2) and conceptual rainfall runoff models (Paper 3). This includes the understanding of optimisation difficulty of these problems according to their features, and how their features change through the change of their problem structure and the types of problems to which they are applied. In the first paper, the computational cost of fitness landscape metrics (explanatory landscape analysis (ELA) metrics) used in computer science is tested and metrics that are suitable for application to water resources problems are identified. Each metric used to understand the features of problems requires a given number of samples, which usually increases with an increase in problem size (dimensionality). Consequently, metrics which require a big increase in sample size through the increase of problem size are not suitable for real-world water resources problems. To identify ELA metrics that have low dependence on problem size, 110 metrics in total are tested on a range of benchmark functions and a number of environmental modelling problems, and 28 are identified to be able to be applied to complex problems without significant increase in computational cost. This finding provides us a new approach to better understand the problem structure of optimisation problems in water resources and has the potential to provide guidance in optimisation algorithm selection for problems in the water resources field. In the second paper, metrics identified to have low dependence on problem size in the first paper are applied to Artificial Neural Network (ANN) model calibration problems. ANN models for different environmental problems with different number of inputs and hidden nodes are used in the test. The environmental problems considered include Kentucky River Catchment Rainfall‐Runoff Data (USA), Murray River Salinity Data (Australia), Myponga Water Distribution System Chlorine Data (Australia), and South Australian Surface Water Turbidity Data (Australia). It is demonstrated that ELA metrics can be used successfully to characterize the features of the error surfaces of ANN models, thereby helping to explain the reasons for an increase or decrease in calibration difficulty, and in doing so, shedding new light on findings in existing literature. Results show that the error surfaces of ANNs with relatively simple structures have a more well-defined overall shape and have fewer local optima, while the error surfaces of ANNs with more complex structures are flatter and have many distributed, deep local optima. Consequently, ANNs with simpler structures can be calibrated successfully using gradient-based methods, such as the back-propagation algorithm, whereas ANNs with more complex structures are best calibrated using a hybrid approach combining metaheuristics, such as genetic algorithms, with gradient-based methods. In the third paper, the ELA metrics identified to have low dependence on problem size in the first paper are applied to Conceptual Rainfall Runoff (CRR) model calibration problems. Different CRRs with different model types, error functions, catchment conditions and data lengths are tested to identify how they affect the features of problem structure, which are related to their model calibration and parameter identification difficulty. It is suggested that ELA metrics can be used to quantify key features of the error surfaces of CRR models, including their roughness and flatness, as well as their degree of optima dispersion. This enables key error surface features to be compared for CRR models with different combinations of attributes (e.g. model structure, catchment climate conditions, error metrics and calibration data lengths and composition) in a consistent, efficient and easily communicable fashion. Results from the application of these metrics to the error surfaces of 420 CRR models with different combinations of the above attributes indicate that model structure differences result in the differences in surface roughness and relative optima dispersion. Additionally, increasing catchment wetness increases the relative roughness of error surfaces, it also decreases optima dispersion. This suggests that model structure and catchment climate conditions can be key issues in affecting the calibration difficulty, efficiency and parameter uniqueness. The experiments conducted in this study also encourage further tests on further CRR models and catchments to identify general patterns between calibration performance, model structure and catchment characteristics.Thesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 202

Using spatiotemporal correlative niche models for evaluating the effects of climate change on mountain pine beetle

Includes bibliographical references.2015 Summer.Over the last decade western North America has experienced the largest mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in recorded history and Rocky Mountain forests have been severely impacted. Although bark beetles are indigenous to North American forests, climate change has facilitated the beetle’s expansion into previously unsuitable habitats. I used three correlative niche models (MaxEnt, Boosted Regression Trees, and Generalized Linear Models) to estimate: (i) the current potential distribution of the beetle in the U.S. Rocky Mountain region, (ii) how this extent has changed since historical outbreaks in the 1960s and 1970s, and (iii) how the potential distribution may be expected to change under future climate scenarios. Additionally, I evaluated the temporal transferability of the niche models by forecasting historical models and testing the model predictions using temporally independent outbreak data from the current outbreak. My results indicated that there has been a significant expansion of climatically suitable habitat over the past 50 years and that much of this expansion corresponds with an upward shift in elevation across the study area. Furthermore, my models indicate that drought was a more prominent driver of current outbreak than temperature, which suggests a change in the climatic signature between historical and current outbreaks. The current climatic niche of the mountain pine beetle includes increased precipitation, colder winter temperatures, and a later spring than the historical climatic niche, which reflects a shift into higher elevation habitats. Projections under future conditions suggest that there will be a large reduction in climatically suitable habitat for the beetle and that high-elevation forests will continue to become more susceptible to outbreak. While all three models generated reasonable predictions (AUC = 0.85 - 0.87), the generalized linear model correctly predicted a higher percentage of current outbreak localities when trained on historical data. My findings suggest that projects aiming to reduce omission error in estimates of future species responses may have greater predictive success with simpler, generalized models