This dissertation is a three-part geographic study of the historical ecology and applied ecological restoration of Lower Putah Creek, California. It uses state-of-the-art tools in geographic information systems (GIS) to analyze historical and modern datasets. Findings are intended to inform riparian management, restoration planning, and restoration design. In Chapter 1, a century of geomorphic change is analyzed using flow frequency analysis and a suite of topographic metrics to characterize channel shape over time. Most notably, it uses height above river (HAR), which is the height (z) of an x-y location above the nearest channel’s thalweg or edge of low-flow (baseflow) river surface. Results show that the dams on Putah Creek reduced channel-forming peak flows by at least 82% in volume, and now maintain a perennial baseflow. Between 1905 and 2005, there was significant channel incision along the entire creek, a change from a U-shaped channel to a V-shaped channel, and a narrowing of the baseflow channel by half, although with significant reach-scale variation. These findings can inform channel-appropriate restoration designs or dam diversion strategies to manage geomorphic processes. In Chapter 2, land cover change is analyzed between 1937 (pre-dam) and 2009 (post-dam) using machine learning image classification tools and forest-based classification methods. Those techniques were used to identify the importance of three topographic variables in predicting land cover: HAR, bank slope, and distance-to-baseflow surface. Results support previous studies, which show that a regulated flow regime enables longer-term plant succession and establishment of woody vegetation. Analysis by topographic variables show that woody vegetation tracked downward channel incision, and that the riparian zone became compressed in a narrower channel with steeper banks. Bank slope, HAR, and distance-to-baseflow surface had equal importance in predicting land cover types in both 1937 and 2009 image classifications. Findings demonstrate that these methods can be used in other systems to characterize vegetation change. In Chapter 3, first, inundation modeling and machine learning image classification are used to establish that HAR is well-correlated to discharge and vegetation. Then, HAR is used in a random forest classification to predict land cover types, and the output classification is used to create HAR zones relevant for restoration planning. Next, 25 reaches of Lower Putah Creek are delineated based on relatively homogeneous geomorphic characteristics and each is then ranked according to the sum of two independent rankings: (1) the in-channel relative area of their combined core riparian and marginal riparian zones; and (2) the in-channel relative area of their combined aquatic and transition zones. While 18 of the 21.22 miles of creek analyzed qualify as “degraded,” lowering of just half of the transition zone to floodplain HAR level could double the riparian zone, indicating a significant opportunity to recover endangered riparian forest in the Sacramento Valley. Finally, the relative area of aquatic and transition zones were used to prescribe geomorphological restoration actions. Of the degraded sections, 13.64 miles qualify for floodplain lowering, 7.05 miles qualify for baseflow narrowing, and 3.21 miles qualify for both, indicating that radical geomorphological change is needed to maximize the riparian habitat potential of Lower Putah Creek. The HAR zones created in this study can be directly incorporated into existing terrain design tools for restoration on Lower Putah Creek, and these methods can be implemented in many other river systems using publicly-available GIS datasets.

This research contributes to solving current bee habitat shortcomings affecting native and naturalized bees in California and identifies strategic habitat best management practices for novel ecosystems. Habitat loss and fragmentation are major causes of decline in bee populations today. Targeted, strategic habitat analysis and modifications could help to boost both habitat connectivity and habitat for bees. First, this research takes a novel approach to habitat analysis by building bee-to-plant habitat relationships foraging models. A presence-only bee-to-plant foraging matrix was compiled from existing literature and contains 23 bee genera and 134 plant genera. This bee foraging matrix was field-tested, and validated to assess its accuracy by weekly observations in 35 gardens at the UC Davis Arboretum and Public Garden over one calendar year. Findings suggest existing ‘pollinator friendly’ plant lists for California bees have significant shortcomings in predicting foraging associations. These results suggest that for the vast majority of bees in California, the existing literature reflects little of their foraging associations. Novel associations demonstrating high foraging frequencies and attracting many bee genera should be considered for improving designed bee habitats. Next, this research uses geographic information systems (GIS) to spatially measure the geographic distances between pollinator foraging plants throughout one year. Assessments of the expected (potential) habitats and observed (utilized) foraging associations further test the ability of recommended plant lists in their ability to attract and sustain bee genera. Examining these results spatially helps to shed light onto how bee habitat fragmentation occurs over a landscape. GIS mapping technology helps to show geographically how bee foraging trends are spatially and temporally distributed, giving further insight into how pollinator habitat networks function and where gaps occur. Comparison between the 35 themed gardens allowed assessment of their value as ecosystems and to gauge the level of pollination ecosystem services they provide. Additionally, due to the linear layout of the Arboretum it is possible to test habitat fragmentation among individual bee genera and the gardens. Since bees vary greatly in their foraging range abilities, those differences in range were accounted for in the mapping model. Spatial habitat analysis is done to compare potential versus actual bee feeding trends and how bee feeding habitat utilization works in a landscape system. Furthermore, the characteristics which make each garden successful as habitat or not are examined. By examining the Arboretum thoroughly from bees’ perspectives, it is possible to identify ecological shortcomings of the Anthroscape and design ecologically strategic and precise habitat solutions to habitat fragmentation. This research aims to boost habitat connectivity and bee population persistence, in doing so, protecting pollination networks and services.

This dissertation is a three-part study of the practice of systematic conservation planning (SCP) on a regional scale in California, necessary for protecting the more than 2,000 plant species and more than 900 animal species considered to be at risk. Natural Community Conservation Plans (NCCPs) pursuant to California Endangered Species Act represent the most powerful tool in statute for such planning, with the highest standards for conserving species. Study results are intended to improve practice in the explicit use of species conceptual models (SCMs) and management-oriented species distribution models (SDMs). Chapter 1 analyzes 18 NCCPs to determine if or how explicit connections were made between both types of models for a covered species and key components of its conservation strategy. Results indicate plans were strong in the use of SDMs, however, each deferred preparing or using SCMs to later management and monitoring phases. A more effective best practice for developing a conservation strategy is to explicitly integrate SCMs and SDMs during plan preparation. Chapter 2 acknowledges the central role scientists play in systematic conservation planning and the decisions they must make regarding management and monitoring in the face of many biological and ecological uncertainties. SCMs are a way for species experts and other stakeholders to share knowledge and document these uncertainties as they determine the most effective conservation strategy for a species. Using San Diego County, California, as a case study, this chapter examines when, how, and by whom SCMs are created and later refined. Keyword searches of planning documents and interviews with scientists revealed that many SCMs have been created but have not yet been formally refined based on monitoring data and stakeholder input. A grounded theory analysis of SCM workshop proceedings and interviews with scientists to determine how and by whom yielded the emergent theory “A Collaborative Ideal: Personalities and Attitudes of Individuals Affect Outcomes and Consensus is Reported.” The chapter concludes with a discussion of best practices for ensuring useful SCMs that reflect species expertise and the input of other stakeholders. Chapter 3 is built on the premise that habitat connectivity is key when designing reserve networks for species conservation. However, acquiring land over time to achieve connectivity for multiple species in a conservation plan can pose a data challenge because of limited species occurrence data and complexity in using multiple species models together. Using an NCCP for Yolo County, California, as a case study, four land acquisition strategies were evaluated in their ability to meet each of three objectives: 1) meet conservation targets, 2) maximize structural habitat connectivity, and 3) maximize connectivity for multiple focal species. The efficiency of each strategy to meet conservation targets was assessed using MARXAN. Structural habitat connectivity of MARXAN solutions for each strategy was analyzed using ‘Contiguity Index’ and ‘Perimeter-Area Ratio’ in FRAGSTATS, and ‘Nearest Neighbor’ in ArcGIS. Focal species connectivity was evaluated by using ‘Cost Connectivity’ in ArcGIS to define species-specific least cost networks and then assessing each network’s conformity with MARXAN solutions. The strategy of acquiring Priority 1 parcels and corridor parcels together provides the best combination for attaining all three objectives. The chapter demonstrates how to use several measures of connectivity in decision-making and recommends using spatial prioritization software often, especially because land acquisition patterns are time sensitive and data may be limited.