Using remote sensing, occupancy estimation, and fine-scale habitat characterization to evaluate fall chum salmon (Oncorhynchus keta) spawning habitat usage in Arctic Alaska

Thesis (M.S.) University of Alaska Fairbanks, 2017Groundwater upwellings provide stable temperatures for overwinter salmon embryo development and this process may be particularly important in cold, braided, gravel-bed Arctic rivers where rivers may freeze solid in the absence of upwellings. Aerial counts and remote sensing were used during 2013-2015 to estimate fall chum salmon (Oncorhynchus keta) spawner abundance states (e.g., low or high), classify river segments by geomorphic channel type (primary, flood, and spring), and map thermal variability along a 25.4 km stretch of the Chandalar River in interior Alaska. Additionally, I used on-the-ground examination of fine scale variation in physical habitat characteristics at 11 representative sites to characterize habitat variability, placed temperature loggers to assess overwinter thermal conditions in redds, and used a developmental model to predict hatching and emergence timing given known spawning dates and incubation temperatures. I delineated 330 unique river segments (mean length = 536 m) and used a multi-season multistate occupancy model to estimate detectability, occupancy, and local colonization and extinction rates. Triplicate surveys performed in 2014 allowed me to estimate detectability and the influence of observer bias. I found that detectability did not vary by observer, channel type, or segment length, but was better for high abundance (0.717 ± 0.06 SE) relative to low abundance (0.367 ± 0.07 SE) aggregations. After correcting for imperfect detection, the proportion of segments occupied by spawning fall chum salmon was highest in 2014 (0.41 ± 0.04 SE), relative to 2013 (0.23 ± 0.04) and 2015 (0.23 ± 0.04). Transition probabilities indicated unoccupied segments were likely to remain so from year to year (2013→2014 = 0.67; 2014→2015 = 0.90), but low abundance spawning segments were dynamic and rarely remained in that state. One-third of high abundance sites remained so, indicating the presence of high quality spawning habitat. Mean segment temperatures ranged from -0.5 to 4.4°C, and occupancy varied positively with temperature. I predicted a 50% probability of occupancy in segments with temperatures of 3°C. With my on-the-ground work, I found that habitat characteristics varied among the three channel types, with most significant differences between main channel and off-channel habitats. Dissolved oxygen and pH decreased with increasing temperature, and conductivity increased with temperature. Predicted hatching and emergence timing ranged from 78 and 176 days (December 11th and March 18th) to 288 and 317 days (July 8th and August 6th), respectively, post-spawning, and were highly variable within sites and among channel types owing to high habitat thermal heterogeneity. Because the Chandalar River supports 30% of the fall chum salmon run in the Yukon River Basin, information such as provided by this study will be critical to allow resource managers to better understand the effects of future climate and anthropogenic change in the region.General introduction -- Chapter 1: A remote sensing and occupancy estimation approach to quantify spawning habitat use by fall chum salmon (Oncorhynchus keta) along the Chandalar River, Alaska -- Chapter 2: Spawning habitat characteristics and phenology of fall chum salmon (Oncorhynchus keta) on the Chandalar River, Alaska -- General conclusions

A novel method for the rapid enumeration of planktonic salmon lice in a mixed zooplankton assemblage using fluorescence

The relative rarity of the planktonic larval stages of salmon lice in comparison to other animals captured in a zooplankton assemblage is an obstacle to estimating their abundance and distribution. Due to the labour intensiveness of standard plankton sorting approaches, the planktonic stages of salmon lice remain understudied and unmonitored despite their importance to the spread of the parasite between salmon farms and to wild salmonids. Alternative methods of identification have been investigated and in a previous study a fluorescence signal was identified. Using filters to target that signal with fluorescence microscopy (excitation/emission wavelengths of 470/525 nm), the salmon louse has a fluorescence intensity 2.4 times greater than non-target animals, which distinguishes it from the zooplankton assemblage and enables rapid enumeration. Here, we present a novel method for the enumeration of planktonic salmon lice larvae, nauplius and copepodid stages, in a mixed zooplankton sample using fluorescence-aided microscopy. Performance of the method was evaluated with a blind trial which found a median accuracy of 81.8% and a mean sample processing time of 31 min. Compared with previously published findings, the novel method provides satisfactory accuracy and enumeration that is more than 20 times faster than traditional light microscopy approaches. Factors influencing the performance of the method are identified and recommendations are made for targeted sampling and automated enumeration

Wild salmon enumeration and monitoring using deep learning empowered detection and tracking

Pacific salmon have experienced declining abundance and unpredictable returns, yet remain vital to livelihoods, food security, and cultures of coastal communities around the Pacific Rim, creating a need for reliable and timely monitoring to inform sustainable fishery management. Currently, spawning salmon abundance is often monitored with in-river video or sonar cameras. However, reviewing video for estimates of salmon abundance from these programs requires thousands of hours of staff time, and data are typically not available until after the fishing season is completed. Computer vision deep learning can enable rapid and reliable processing of data, with potentially transformative applications in salmon population assessment and fishery management. Working with two First Nations fishery programs in British Columbia, Canada, we developed, trained, and tested deep learning models to perform object detection and multi-object tracking for automated video enumeration of salmon passing two First Nation-run weirs. We gathered and annotated more than 500,000 frames of video data encompassing 12 species, including seven species of anadromous salmonids, and trained models for multi-object tracking and species detection. Our top performing model achieved a mean average precision (mAP) of 67.6%, and species-specific mAP scores > 90% for coho and > 80% for sockeye salmon when trained with a combined dataset of Kitwanga and Bear Rivers’ salmon annotations. We also tested and deployed a prototype for a real-time monitoring system that can perform computer vision deep learning analyses on site. Computer vision models and off-grid monitoring systems show promise for automated counting and species identification. A key future priority will be working with stewardship practitioners and fishery managers to apply salmon computer vision, testing and applying edge-capable computing solutions for in-situ analysis at remote sites, and developing tools for independent user-led computer vision analysis by non-computer scientists. These efforts can advance in-season monitoring and decision making to support adaptive management of sustainable wild salmon fisheries

The salmon louse larval black box: evaluating fecundity and enumerating planktonic stages with an aquaculture management perspective

Modern salmon aquaculture began in 1970 with the innovation of at-sea fish pens which precipitated a rapid growth in production. The expansion of the industry and increased number of farmed fish concentrated within the open net-pens has produced conditions that foster environmental and disease problems. Among the various pathogens impacting the industry, the salmon louse (Lepeophtheirus salmonis) presents a unique challenge due to its proliferation on farms, welfare impacts on host fish, the threat it poses to wild populations of salmonids, and for the cost of and its resistance to control efforts. Norway, the world leader in salmon production, has responded to the persistent challenge of salmon lice with the implementation of a management regime (Traffic Light System) that links permitted aquaculture production to louse induced mortality of wild Atlantic salmon populations. Those management decisions are reliant on an understanding of salmon louse distribution throughout the Norwegian coast, but aspects of the copepod’s life history and biology which determine their planktonic abundance remain understudied. Nevertheless, to meet the needs of the management regime modelers must forecast salmon louse reproduction and planktonic dispersal from salmon farms. Although these models are validated with observations of salmon louse infections on fish, there is a lack of empirical evidence on the distribution and abundance of planktonic stages. Due to the difficulty of enumerating planktonic lice in a mixed zooplankton sample they are almost unobservable and thus exist in a ‘black-box’. This thesis seeks to shed light on the salmon louse larval black-box within the context of the aquaculture management in Norway through two approaches. A greater knowledge of the planktonic stages can be gained through a better understanding of the salmon louse’s life history, and through empirical data on their planktonic abundance and distribution. This thesis addresses the first approach by refining the current understanding of salmon louse fecundity and the second through the development of a novel method for enumeration of planktonic stages. In paper I, we investigated fecundity by examining egg clutch size of salmon lice collected from farmed salmon, wild salmon, and sea trout from multiple farms and fields sites throughout Norway. The investigation revealed the predominant determinate of clutch size is the body size of females, which is dependent on rearing temperature. We further found that a third of adult female lice on farmed salmon were not sexually mature and 10% of the mature females were not egg-bearing. The female lice parasitizing sea trout were less fecund then those on Atlantic salmon with lower rates of egg-bearing and smaller clutch sizes. In papers II and III, we develop a novel method of planktonic salmon lice enumeration which used fluorescence to differentiate th e lice within a mixed zooplankton sample so they could be rapidly identified. First the fluorescence profiles of lice and non-target copepods were examined to identify a unique and reliable fluorescence signal, then a methodology using that signal was developed and tested. The fluorescence signal was found to be strongest using an excitation wavelength of 470 nm and an emission filter of 525 nm. After storage in formalin preservation the salmon lice copepodids had a fluorescence intensity that was 2.4 times greater than non-target copepods. When a mixed zooplankton sample was illuminated with the excitation light the salmon lice would fluoresce brighter than most other animals in the sample and could be quickly discovered. Participants in a blind trial processed standard zooplankton samples in a mean of 31 minutes and identified the lice with an accuracy of 82%. Compared to traditional taxonomic identification, the novel method was 20 times faster, thus providing a practical tool for the study of lice and monitoring of their planktonic stages. The management of salmon aquaculture is dependent on accurate understanding and modeling of the distribution of planktonic salmon lice. The work of this thesis can reduce the inherent uncertainties of those models through better parameterization and through a new tool which enables validation with direct observation of planktonic abundance. However, for the aquaculture industry to continue to grow in Norway this thesis concludes that a prevention priority must replace the current paradigm of salmon louse control through treatment.Doktorgradsavhandlin

Population Genetics And Mixed Stock Analysis Of Chum Salmon (Oncorhynchus Keta) With Molecular Genetics

Thesis (Ph.D.) University of Alaska Fairbanks, 2012Chum salmon (Oncorhynchus keta) are important for subsistence and commercial harvest in Alaska. Variability of returns to western Alaskan drainages that caused economic hardship for stakeholders has led to speculation about reasons, which may include both anthropogenic and environmental causes in the marine environment. Mixed stock analysis (MSA) compares genetic information from an individual caught at sea to a reference baseline of genotypes to assign it to its population of origin. Application of genetic baselines requires several complex steps that can introduce bias. The bias may reduce the accuracy of MSA and result in overly-optimistic evaluations of baselines. Moreover, some applications that minimize bias cannot use informative haploid mitochondrial variation. Costs of baseline development are species-specific and difficult to predict. Finally, because populations of western Alaskan chum salmon demonstrate weak genetic divergence, samples from mixtures cannot be accurately assigned to a population of origin. The chapters of this thesis address three challenges. The first chapter describes technical aspects of genetic marker development. The second chapter describes a method to evaluate the precision and accuracy of a genetic baseline that accepts any type of data and reduces bias that may have been introduced during baseline development. This chapter also includes a method that places a cost on baseline development by predicting the number of markers needed to achieve a given accuracy. The final chapter explores the reasons for the weak genetic structure of western Alaskan chum salmon populations. The results of those analyses and both geological and archaeological data suggest that recent environmental and geological processes may be involved. The methods and analyses in this thesis can be applied to any species and may be particularly useful for other western Alaskan species