Criteria used to characterize lotic salmonid habitat are often based on observed correlations between physical habitat characteristics and salmonid abundances. A focus on physical habitat features ignores other habitat components, such as an adequate supply of food that set the physiological limitations on salmonid growth and survival. This study outlines the development of a habitat assessment approach that focuses on how invertebrate food availability interacts with stream temperatures to determine salmonid growth potentials. Abundances of benthic and drifting invertebrate communities, stream temperatures, and juvenile steelhead trout (Onchorhynchus mykiss gairdneri) summer growth rates and abundances were measured within 10 distinct stream segments in central Oregon. Stream temperatures and growth rates were used as inputs for bioenergetics model simulations to produce estimates of O. mykiss summer consumption rates. Measures of invertebrates providing the best description of food availability were chosen based on their ability to explain observed variation in salmonid consumption. Much of the variation in O. mykiss consumption estimates was explained by measurements of total drift biomass along a type II predator response curve. A random effects analysis of variance (ANOVA) was used to partition variation in invertebrate abundances across spatial and temporal scales. Quantification of variation at multiple scales allowed identification of a relevant spatial scale at which to assess macroinvertebrates relevant to salmonid populations, and compare the precision associated with measures of benthic and drifting invertebrate abundances. Results suggested that spatial variation in drifting and benthic invertebrate abundances are greatest at the scale of streams. Total drift biomass and total benthic biomass were more precise at the stream and stream reach scale than drift and benthic density. The information provided by this study will be used to guide the development of sampling approaches that describe invertebrates in a manner more directly related to salmonid production.

Wireless sensor networks (WSNs) represent a class of miniaturized information systems designed to monitor physical environments. These smart monitoring systems form collaborative networks utilizing autonomous sensing, data-collection, and processing to provide real-time analytics of observed environments. As a fundamental research area in pervasive computing and envisioned as large-scale autonomous networks of communicating nodes capable of monitoring conditional metrics over vast geographic areas, WSNs have the potential to provide researchers and conservationists with increased knowledge of the intricacies and interrelationships of disparate environments. The author addressed the problem of developing a methodology for the design and deployment of WSNs in uncontrolled and harsh outdoor environments. Within the context of a research and conservation field study of flora, the author developed a model for deployment of WSNs within the domain of microclimate habitat monitoring. The goal of this study was to contribute to the body of knowledge in WSN research by developing a model for deployment that was scientifically sound and replicable. To accomplish this goal, the author conducted an investigation of current technologies associated with WSNs, their capabilities, and their applications specific to the stated domain. To validate this model, the author deployed a WSN for monitoring the microclimate habitats of a population of Spiranthes lacera var. gracilis, common name, slender ladies' tresses. During this field study, the WSN performed according to design and produced sufficient data to provide an accurate representation of the microclimate habitats of the objects of study. As a contribution to the WSN research body of knowledge, the author used an SDLC methodology to provide a pragmatic approach to deployment focused on the elements of nuance specific to WSNs for microclimate habitat monitoring.

This study explores leadership dynamics in collaborative governance. The research features a collaboration case study of sixteen federal and state agency and NGO stakeholders. The collaboration is conducting a ten-year, basin-scale monitoring project of salmonid habitat restoration projects in the Middle Fork John Day (MFJD) River basin in Eastern Oregon. The monitoring project is known as an intensively monitored watershed (IMW), one of sixteen throughout the Pacific Northwest. The research is guided by the following question: How do leadership dynamics in the MFJD IMW collaborative governance structure facilitate effective collaborative process or create limitations to that process? This study uses qualitative research methods in evaluating multiple research sources. Insights from this study may prove valuable in providing guidance on effectively structuring and managing basin-scale collaborative habitat monitoring projects, including future IMW projects. This study further aims to contribute to research on collaborative leadership for the greater scholarship on collaboration.

Anthropogenic climate and land use change is causing rapid shifts in the distribution and composition of habitats with profound impacts on ecosystem biodiversity. The sustainable management of ecosystems requires monitoring programmes capable of detecting shifts in habitat distribution and composition at large spatial scales. Remote sensing observations facilitate such efforts as they enable cost-efficient modelling approaches that utilize publicly available datasets and can assess the status of habitats over extended periods of time. In this study, we introduce a modelling framework for habitat monitoring in Germany using readily available MODIS surface reflectance data. We developed supervised classification models that allocate (semi-)natural areas to one of 18 classes based on their similarity to Natura 2000 habitat types. Three machine learning classifiers, i.e., Support Vector Machines (SVM), Random Forests (RF), and C5.0, and an ensemble approach were employed to predict habitat type using spectral signatures from MODIS in the visible-to-near-infrared and short-wave infrared. The models were trained on homogenous Special Areas of Conservation that are predominantly covered by a single habitat type with reference data from 2013, 2014, and 2016 and tested against ground truth data from 2010 and 2019 for independent model validation. Individually, the SVM and RF methods achieved better overall classification accuracies (SVM: 0.72–0.93%, RF: 0.72–0.94%) than the C5.0 algorithm (0.66–0.93%), while the ensemble classifier developed from the individual models gave the best performance with overall accuracies of 94.23% for 2010 and 80.34% for 2019 and also allowed a robust detection of non-classifiable pixels. We detected strong variability in the cover of individual habitat types, which were reduced when aggregated based on their similarity. Our methodology is capable to provide quantitative information on the spatial distribution of habitats, differentiate between disturbance events and gradual shifts in ecosystem composition, and could successfully allocate natural areas to Natura 2000 habitat types.

Rivers and their catchments are under mounting pressure from direct channel modification, intensification of land use, and from a legacy of decades of channelisation. Recent legislation, in the form of the EU Water Framework Directive, places a greater emphasis on the management of water bodies as holistic systems, and includes the explicit consideration of hydromorphological quality, which describes the hydrologic and geomorphic elements of river habitats. These are defined specifically as hydrological regime, river continuity and river morphology. This appreciates that sediment and flow regimes, along with the channel structure, provides the 'template' on which stream ecological structure and function is built. Invertebrate fauna contribute significantly to the biodiversity of rivers, and often form the basis of monitoring river health. However much of the fundamental ecological knowledge base on the response of invertebrates to hydromorphological change needed to make informed decisions and accurate predictions, is either lacking, inadequate or contradictory. This thesis addresses some of the key potential shortcomings in recent bio-assessment that others have alluded to, but which have rarely been explored in the context of direct channel manipulations. By using two case studies of, realignment in a natural upland catchment, and flood protection engineering in an urban stream, this study investigates the sensitivity of hydromorphological impact assessment methods that rely on biodiversity patterns of benthic riverfly (Ephemeroptera, Plecoptera and Trichoptera) larva. This work employed widely used biomonitoring indices of benthic riverfly larva abundance, species richness, alpha and beta diversity, and community composition, applied over a range of spatial scales, in combination with spatially contemporaneous physical habitat data, to describe and explain community changes in response to disturbance, and patterns of natural variation. The effects of restoration were investigated using a high degree of sample replication within channels and across the wider catchment, as well as contrasting spring and autumn seasons. To assess change in a small urban channel, approaches that explicitly consider spatial elements of community data, using spatial eigenvectors analysis, were applied to spatially detrend community data and directly investigate spatial patterns. Restoration of the Rottal Burn was found to be successful in restoring habitat diversity and geomorphic processes, and in turn increasing reach scale species richness and beta diversity through the gradual arrival of rare and specialist taxa into novel habitats. Catchment scale replication revealed high variation in diversity indices of modified and undisturbed streams, and a strong temporal pattern related to antecedent flow conditions. Channels with greater habitat heterogeneity were able to maintain high gamma diversity during times of high flow stress by providing a number of low flow refuges along their length. The urban Brox Burn had surprisingly high riverfly richness and diversity driven by small scale hydraulic heterogeneity, created by bed roughness resulting in a range of microhabitats. Riverfly community responses to direct channel dredging could not be detected by measurements of average richness and diversity, however distinct changes were seen in gamma diversity, the identity of community members and their arrangement among sample patches. Impacts of sediment pollution release due to engineering were short lived and apparently had little detrimental impact on biodiversity. Strong spatial patterns of community assembly on the stream bed were uncovered, relating to longitudinal, edge and patchy patterns. Significant habitat drivers of community composition were confounded by high amounts of spatial autocorrelation, especially hydraulic variables. Due to the strongly physical and spatial nature of hydromorphological disturbance, turnover of species between sample locations at a range of scales, and the spatial arrangement of habitats and communities is of more use for detecting these types of subtle changes compared to mean richness or diversity. These findings have implications for the targeting of resources for monitoring of restoration, or engineering disturbances, in order to be sensitive to hydromorphological change. Efforts should target the main area of natural variability within the system, either replicating sampling in time or space to distinguish effects of impact. Spatial patterns, measures of beta diversity and species identity can be better exploited to identify systems with functioning geomorphological processes. Channel typologies proved misleading, and quantification of habitat and selection of control sites using multiple pre-defined criteria should be carried out. Studies of restoration operations and engineering impacts provide considerable opportunities for advancing our knowledge of the mechanisms that drive community response under a range of conditions to improve impact detection.