Forest-dwelling amphibians contribute to diverse ecosystem services in Ontario but are threatened by habitat degradation and fragmentation. My thesis investigated key amphibian habitats in Ontario forests with the goal of providing resource managers with tools and techniques to protect and restore amphibian populations. I primarily focussed on amphibians that breed in temporary forested wetlands known as vernal pools, as these wetlands are overlooked in provincial legislature and particularly sensitive to changes in land-use and climate. First, I investigated the distribution and community structure of vernal pools in forests of eastern Georgian Bay, Lake Huron. I developed an accurate remote sensing technique to map vernal pool habitat using readily available spatial data and found that undocumented vernal pools accounted for over half of the wetlands in the region. I documented the importance of the length of pool inundation (hydroperiod) and canopy openness in determining the composition of vernal pool amphibian assemblages. In particular, pools with short hydroperiods and closed canopies tended to support only early breeders and canopy generalists. Next, I used two case studies to demonstrate the usefulness of existing amphibian occurrence datasets, specifically for the improvement of habitat mapping and monitoring. For the first case study, I created habitat suitability models using known locations of the endangered Jefferson salamander (Ambystoma jeffersonianum). Models yielded good discriminatory ability between presence and pseudo-absence data and confirmed the importance of deciduous/mixed forests as key habitats. Habitat suitability maps revealed potential undocumented habitat in the Greenbelt region of Ontario. For the second case study, I developed optimizations of time and effort for a salamander monitoring program. I verified the need for at least ten years’ worth of monitoring data for reliable trend detection and demonstrated that the precision and accuracy of occupancy estimates are dependent on the allocation of effort across monitoring sites and repetitions. / Thesis / Doctor of Philosophy (PhD) / Amphibian declines represent one of the hallmarks of the current biodiversity crisis. While there are many factors responsible for amphibian declines, the most significant threats are habitat loss and degradation. This Ph.D. thesis describes amphibian habitat use in Ontario forests and provides resource managers with tools and techniques to protect habitat. Using satellite imagery, I developed a strategy to map small amphibian breeding wetlands (vernal pools) in forests of eastern Georgian Bay. I also identified the importance of pool drying times and forest canopy cover to the amphibians that breed in these wetlands. In the fragmented forest patches of southern Ontario, I mapped suitable habitat for the endangered Jefferson salamander and identified the importance of large deciduous/mixed forests. Lastly, I assessed the effectiveness of a long-term salamander monitoring program in southern Ontario and demonstrated the use of techniques to optimize the allocation of effort and maximize the accuracy of monitoring results.

Soil moisture is an essential climate variable and provides critical state information for hydrological applications. The state of soil moisture influences the exchange of water and energy between the earth surface and the atmosphere, partitions infiltration and runoff, can limit the net primary productivity of a region and govern the dynamics of geochemical processes. Satellite observations can be used to provide information about this important variable but are often available at a scale that is far greater than most hydrological processes. The scope of the research presented in this dissertation was to identify practical methods to facilitate the use of coarse scale satellite soil moisture information in higher resolution hydrological and land-surface modelling applications. Research was primarily conducted in the Hamilton-Halton watershed of Southern Ontario, Canada, although other watersheds and datasets were periodically used in some chapters. A comprehensive review was conducted on the use of high resolution soil moisture information for hydrological applications, and data assimilation was identified as the most common method for integrating soil moisture information into a hydrological model. It was also identified that most watersheds displayed the property of temporal persistence and that root-zone soil moisture was of greater importance than surface soil moisture (Appendix B). In light of this information, the focus of this research was the downscaling of soil moisture and brightness temperature (TB) observations from the Soil Moisture and Ocean Salinity (SMOS) passive microwave satellite. Satellite observations are sensitive to surface soil moisture, while rootzone soil moisture provides the greatest benefit to hydrological and land surface applications. To overcome this discrepancy, artificial neural networks (ANN) were evaluated as a method to estimate rootzone soil moisture from surface observations that accounted for the known non-linearities of soil moisture processes. The ANN model was trained with a numerical soil moisture physics model and validated using in situ observations from the McMaster Mesonet and USDA SCAN sites. The ANN was capable of accurately depicting the rootzone soil moisture based on its training data at multiple sites, but was limited when the temporal distribution of soil moisture at a particular site was considerably different than the training data. Therefore, with the appropriate training data, ANNs are a viable method for predicting rootzone soil moisture from surface observations such as those available from satellites. To provide high resolution soil moisture information from coarse resolution satellite data, bias correction was proposed and evaluated as a downscaling method for both soil moisture and TB. Using in situ data from two well instrumented USDA watersheds and a hydrological land-surface scheme (HLSS), it was found that temporal evolution of both soil moisture and TB at fine scale (~1 km) could be well characterized by the temporal evolution of the coarse scale (~20 km) soil moisture and TB. The fine scale spatial distribution of soil moisture could be predicted with a high degree of skill by correcting the bias between the coarse and fine scale soil moisture/TB. In studying the correction of biases, it was found that naïve application of bias correction methods could result in the introduction of multiplicative biases in the bias corrected dataset. The theoretical implications of this for a data assimilation system were discussed although not yet evaluated. A bootstrap resampling approach was evaluated as a solution to this problem and it was found that resampled data could result in a robust bias correction that eliminated additive bias in most instances while limiting the induction of multiplicative bias. This new method was found to significantly outperform the standard bias correction techniques. / Thesis / Doctor of Philosophy (PhD) / Soil moisture is an important hydrological variable. The state of soil moisture controls the partition between the runoff and infiltration as well as the exchange of heat from the surface to the atmosphere. Therefore, an accurate depiction of the state of soil moisture is important for producing accurate flood and drought forecasts, numerical weather prediction and agricultural forecasts. The state of soil moisture can be observed from space using microwave remote sensing measurements. However, the resolution of most passive microwave observations, such as those from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) satellite are at a resolution of approximately 40 km which is far more coarse than the approximately 1 km resolution of most hydrological processes. The work in this thesis presented bias correction methods as a mean to match the spatial scale of the satellite observations to high resolution hydrological and land surface models. These data were generated and compared using an advanced land surface hydrological scheme under development at Environment Canada. It was found that simple bias correction methods were capable of effectively downscaling SMOS observations to the a scale of 1 km without the loss of information from the satellite. A new bias correction method was also presented that was found to significantly outperform standard techniques.

The social, economic, and ecological costs of plant invasions are vast, through their ability to alter ecosystem structure and function. Invasive annual grasses are a nuisance in the American Southwest through promotion of the grass-fire cycle. Annual grasses such as Bromus rubens, Bromus tectorum, Schismus barbatus, and Schismus arabicus have invaded the Mojave Desert and increased fire occurrence, thus it is important to identify and characterize the areas where persistent invasion has occurred and subsequently fire risk is increased by understanding the distribution of these invasive grasses. Previous plot and landscape-scale research has revealed anthropogenic and biophysical correlates with the establishment and dominance of invasive annual grasses in the Mojave Desert. However, these previous studies have been limited in spatial and temporal scales. Here we use a remote sensing framework to map persistent and productive populations of invasive annual grass, called hot spots, in the entire Mojave Desert ecoregion over 12 years, identify important variables for predicting hot spot distribution, and identify the most invaded subregions. Hot spots were identified in over 5% of the Mojave Desert, and invasive grasses were detected in over 90% of the desert at least once. Our results indicate that soil texture, aspect, winter precipitation, and elevation are the most important predictive variables of invasive grass hot spots, while anthropogenic variables were the least useful. The most invaded subregions of the Mojave Desert were western Mojave basins, eastern Mojave mountain woodland and shrubland, western Mojave low ranges and arid footslopes, eastern Mojave basins, and eastern Mojave low ranges and footslopes.

Peat is an accumulation of soil formed from partially decomposed organic matter. Peat can burn, especially in hot, dry weather which is happening more often due to climate change; smouldering releases stored carbon to the atmosphere. Peat that has higher organic bulk density and lower moisture content is more vulnerable to fire: it will burn more severely (more deeply) if ignited. Shallower peat is less able to retain moisture during droughts and is therefore likely more vulnerable to fire; however, mapping peat depths at high spatial resolution is expensive or requires extensive fieldwork. This project uses remote sensing in combination with machine learning to estimate peat depth across a peatland and rock barren landscape. A Random Forest model was used to map peat depths across the landscape at a 1 m spatial resolution using LiDAR data and orthophotography. The resulting map was able to predict peat depths (R2 = 0.73, MAE = 28 cm) and showed that the peat depths which are especially vulnerable to high severity fire are distributed in numerous small patches across the landscape. This project also examined peat bulk density and found that the Von Post scale for peat decomposition can be used as a field method for estimating bulk density (R2 = 0.71). In addition, in this landscape, peat bulk densities at the same depth (within the top 45 cm) are higher in shallower peat because in shallower peat, more decomposed peat was found closer to the surface, and because peat with high mineral content was found close to the bedrock or mineral soil. The findings of this project will be valuable for wildfire managers to determine which areas on the landscape are most vulnerable to fire, allowing them to mobilize resources more rapidly for wildfire suppression. / Thesis / Master of Science (MSc) / Peat is organic soil made from decomposing plant material. Peat can burn, especially in the hot, dry weather which is happening more often due to climate change. Dense, dry peat is more vulnerable to fire: it will burn more deeply. Because it is known that areas with deeper peat can retain moisture better, peat depth can be used as a proxy for vulnerability to fire. Since peat depth is expensive and time consuming to map directly, remotely sensed data such as aerial imagery was used in a model to predict peat depths. The model was able to predict peat depths and displayed that the most vulnerable areas are scattered across the landscape in small patches. This project also found that denser peat is found farther from the surface in deeper peat areas, further supporting the use of peat depth as a proxy for vulnerability to smouldering.

Previous research efforts have shown that fish and macroinvertebrates are responsive to fine sediment in streambeds. Excess fine sediment (<2mm in diameter) impairs over 40,000 miles of streams in the U.S., degrading habitat quality for many aquatic species. Embeddedness (emb, %), a measure of fine sediment in gravel bed streams, is negatively correlated with bankfull shear velocity (u*, m/s). This relationship can be modeled by emb = au*b, with baseline coefficient values of a = 10 and b = –1. The purpose of this thesis was to investigate the applicability of this relationship across the U.S., to begin to quantify the variation of embeddedness in time, and to determine the applicability of embeddedness as a habitat metric for lotic biota. The areas that were studied included Stroubles Creek at the Virginia Tech Stream Lab, the Upper Roanoke River Basin in southwest Virginia, and Level II and III ecoregions nationwide with the U.S. EPA National Rivers and Streams Assessment dataset. Nationally, measurements of embeddedness were higher than modeled in areas with higher sediment supply, and lower than modeled in regions with low fine sediment supply. By calculating shear velocity through remotely sensed channel geometry metrics, embeddedness may be predicted throughout a stream network. Various biotic metrics were found to be correlated to embeddedness, with regional variation. Burrowing macroinvertebrate taxa, which may use increased sand to escape predation, increased with increasing embeddedness while the number of Ephemeroptera, Plecoptera, Trichoptera (EPT) taxa, the number of lithophilic spawning fish, and the number of salmonid taxa decreased with increasing embeddedness. Highly embedded substrate is generally considered poor habitat, which was supported by a trend of decreasing intolerant fish taxa with increasing embeddedness. Richness (total number of taxa) did not show a significant correlation, indicating that embeddedness, and fine sediment in general, is not necessarily an impairment to all stream habitat, but is impactful for particular taxa. / Master of Science / Previous research has shown that fish and macroinvertebrates are responsive to fine sediment in streambeds. Excess fine sediment (sand, silt, and clay) impairs over 40,000 miles of streams in the U.S., degrading habitat quality for many aquatic species. Embeddedness (emb, %), a measure of fine sediment in gravel bed streams, decreases with increasing bankfull shear velocity (u*, m/s), a measure of a stream's ability to move a particular size of sediment. The purpose of this thesis was to investigate the relationship between embeddedness and shear velocity in varying areas, to begin to quantify the variation of embeddedness in time, and to determine the applicability of embeddedness as a habitat metric for stream biota. The areas that were studied included Stroubles Creek at the Virginia Tech Stream Lab, the Upper Roanoke River Basin in southwest Virginia, and Level II and III ecoregions nationwide with the U.S. EPA National Rivers and Streams Assessment dataset. Nationally, measurements of embeddedness were higher in areas that may have higher sediment supply, and lower in regions with low fine sediment supply. By calculating shear velocity with remotely available stream data, embeddedness may be predicted throughout a stream network and compared with biota in those locations. Various biotic metrics were found to be correlated to embeddedness, with regional variation. Burrowing macroinvertebrate taxa, which may use increased sand to escape predation, increased with increasing embeddedness while the number of Ephemeroptera, Plecoptera, Trichoptera (EPT) taxa, the number of lithophilic spawning fish, and the number of salmonid taxa decreased. Highly embedded substrate is generally considered poor habitat, which was supported by a trend of decreasing intolerant fish taxa with increasing embeddedness.

Indiana University-Purdue University Indianapolis (IUPUI) / This project examines the impact of confounding variables that have limited the accuracy of remotely predicting phycocyanin in three Indiana drinking and recreational water reservoirs. In-situ field reflectance spectra were collected from June to November 2006 over a wide range of algal bloom conditions using an ASD Fieldspec (UV/VNIR) spectroradiometer. Groundtruth samples were analyzed for chlorophyll a, phycocyanin, total suspended matter, and other water quality constituents. Previously published spectral algorithms for the detection of phycocyanin were evaluated against lab measured pigment concentrations using linear least squares regression. Algorithm performance varied across study sites (best performing models by reservoir resulted in r2 values of 0.32 to 0.84). Residuals of predicted versus measured pigment concentrations were analyzed against concentration of potential confounding water constituents. Residual analysis revealed optically active constituents contributed between 25% and 95% of original phycocyanin model errors. Inclusion of spectral variables into models to account for significant confounders resulted in improved spectral estimates of phycocyanin (r2 = 0.56 to 0.93).

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The islands of Bonaire and Curacao, Dutch Caribbean, were both mapped along their leeward coasts for dominant coral community and other benthic cover in the early 1980s. This mapping effort offers a unique baseline for comparing changes in the benthic community of the two islands since that time, particularly given the marked differences between the two islands. Bonaire is well-protected and completely surrounded by a marine protected area (MPA), which includes two no-diving marine reserves; additionally, Bonaire's population is only around 15,000. In contrast, the island of Curacao is home to 140,000 inhabitants and marine protection is limited, with a reef area of 600 ha established as a "paper" park (i.e., little enforcement). Video transects collected by SCUBA over the reefs were collected on Bonaire in January of 2008; when compared to data from 1985, coral cover had declined in the shallowest portion of the reef (< 5 m) and was mostly the result of declines in Acropora spp., whereas head corals increased. Transects closest to the no-diving marine reserves showed higher coral cover and diversity than transects located farther from the reserves. Satellite remote sensing techniques were used to create landscape-scale reef maps along the leeward coasts of both islands, which could differentiate areas of high hard coral cover (> 20%), predominantly sand (> 50%) and areas where hard coral and sand were mixed with soft corals, sea whips and marine plants. These modern maps (2007-09) were groundtruthed using the video data collected on Bonaire for accuracy and then compared to the early 1980s maps of the reefs on both islands. Bonaire experienced declines in coral cover overall and the remaining coral was increasingly patchy; however, changes in patch characteristics were not significant over the time period, but status as a marine reserve and the sheltering of the shoreline did appear to buffer against coral loss. Surprisingly, the island of Curacao did not experience a decline in total coral cover, but did become increasingly patchy, significantly more so than Bonaire. The Curacao Underwater Park afforded no additional protection against coral loss or fragmentation than an adjacent unprotected area of reef. The difference between the two islands in coral loss versus fragmentation has the potential for a unique natural experiment to study the effects of habitat fragmentation in the absence of overall habitat loss at the landscape scale. The Bonaire National Marine Park could benefit by restricting visitors to its most frequented dive sites by increasing the cost of entry into a tiered pay system, thus generating more income for education and management of the park, as well as deterring some divers from these overused sites. Satellite remote sensing-derived maps are useful for rapid reef mapping and can be utilized for comparison to ancillary maps created by more traditional methods. Satellite-derived maps can only distinguish benthic habitats coarsely (3-4 habitat classes) and are only as reliable as their source data, they benefit greatly from fieldwork to determine depth, geographic location, and benthic habitat cover in real time.

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