Small unmanned aircraft systems (sUASs) are fostering novel approaches to marine mammal research, including baleen whale photogrammetry, by providing new observational perspectives. We collected vertical images of 89 gray and 6 blue whales using low cost sUASs to examine the accuracy of image based morphometry. Moreover, measurements from 192 images of a 1 m calibration object were used to examine four different scaling correction models. Results indicate that a linear mixed model including an error term for flight and date contained 0.17 m less error and 0.25 m less bias than no correction. We used the propagation uncertainty law to examine error contributions from scaling and image measurement (digitization) to determine that digitization accounted for 97% of total variance. Additionally, we present a new whale body size metric termed Body Area Index (BAI). BAI is scale invariant and is independent of body length (R2 = 0.11), enabling comparisons of body size within and among populations, and over time. With this study we present a three program analysis suite that measures baleen whales and compensates for lens distortion and corrects scaling error to produce 11 morphometric attributes from sUAS imagery. The program is freely available and is expected to improve processing efficiency and analytical continuity.

Baleen whales store energy gained on foraging grounds to support reproduction and other metabolic needs while fasting for long periods during migration. Whale body condition can be used to monitor foraging success, and thus better understand and anticipate individual-and population-level trends in reproduction and survival. We assessed the body condition of eastern North Pacific gray whales (Eschrichtius robustus) on their foraging grounds along the Oregon coast, USA, from June to October of three consecutive years (2016)(2017)(2018). We used drone photogrammetry and applied the body area index (BAI) to measure and compare whale body condition, which is a continuous, unitless metric similar to the body mass index in humans. A total of 289 drone flights were carried out over 106 photo-identified whales, which were grouped into demographic units by sex, maturity, and female reproductive status. Calves and pregnant females displayed the highest BAIs, followed by resting females, mature males, and, finally, lactating females, reflecting the significant energetic demands on reproductive females. In all three years, gray whale body condition improved with the progression of feeding seasons, demonstrating the accumulation of body energy reserves on the foraging grounds. Yet, body condition was significantly better in 2016 than in 2017 and 2018 when overall body depletion was observed, indicating a difference in prey availability and/or quality between years. We analyzed local upwelling patterns between 2013 and 2018 as an oceanographic proxy for prey and determined significantly greater upwelling between 2013 and 2015 than low upwelling years between 2016 and 2018. We hypothesize that these upwelling patterns created ecosystem shifts in primary productivity and zooplankton prey of gray whales, causing carry-over effects between foraging success and body condition in subsequent years. This study demonstrates the value of monitoring whale body condition to better understand temporal variation in foraging success, and potentially detect and describe the causes of anomalous changes in whale population health, such as the 2019 gray whale mortality event.

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Degraded floodplains and valley floors are restored with the goal of enhancing habitat for native fish and aquatic-riparian biota and the protection or improvement of water quality. Recent years have seen a shift toward “process-based restoration” that is intended to reestablish compromised ecogeomorphic processes resulting from site- or watershed-scale degradation. One form of process-based restoration has developed in the Pacific Northwest, United States, that is intended to reconnect rivers to their floodplains by slowing down flows of sediment, water, and nutrients to encourage lateral and vertical connectivity at base flows, facilitating development of dynamic, self-forming, and self-sustaining river-wetland corridors. Synergies between applied practices and the theoretical work of Cluer and Thorne in 2014 have led this form of restoration to be referred to regionally as restoration to a Stage 0 condition. This approach to rehabilitation is valley scale, rendering traditional monitoring strategies that target single-thread channels inadequate to capture pre- and post-project site conditions, thus motivating the development of novel monitoring approaches. We present a specific definition of this new type of rehabilitation that was developed in collaborative workshops with practitioners of the approach. Further, we present an initial synthesis of results from monitoring activities that provide a foundation for understanding the effects of this approach of river rehabilitation on substrate composition, depth to groundwater, water temperature, macroinvertebrate richness and abundance, secondary macroinvertebrate production, vegetation conditions, wood loading and configuration, water inundation, flow velocity, modeled juvenile salmonid habitat, and aquatic biodiversity.

To understand how predators optimize foraging strategies, extensive knowledge of predator behavior and prey distribution is needed. Blue whales employ an energetically demanding lunge feeding method that requires the whales to selectively feed where energetic gain exceeds energetic loss, while also balancing oxygen consumption, breath holding capacity, and surface recuperation time. Hence, blue whale foraging behavior is primarily driven by krill patch density and depth, but many studies have not fully considered surface feeding as a significant foraging strategy in energetic models. We collected predator and prey data on a blue whale (Balaenoptera musculus brevicauda) foraging ground in New Zealand in February 2017 to assess the distributional and behavioral response of blue whales to the distribution and density of krill prey aggregations. Krill density across the study region was greater toward the surface (upper 20 m), and blue whales were encountered where prey was relatively shallow and more dense. This relationship was particularly evident where foraging and surface lunge feeding were observed. Furthermore, New Zealand blue whales also had relatively short dive times (2.83 ± 0.27 SE min) as compared to other blue whale populations, which became even shorter at foraging sightings and where surface lunge feeding was observed. Using an unmanned aerial system (UAS; drone) we also captured unique video of a New Zealand blue whale's surface feeding behavior on well-illuminated krill patches. Video analysis illustrates the whale's potential use of vision to target prey, make foraging decisions, and orient body mechanics relative to prey patch characteristics. Kinematic analysis of a surface lunge feeding event revealed biomechanical coordination through speed, acceleration, head inclination, roll, and distance from krill patch to maximize prey engulfment. We compared these lunge kinematics to data previously reported from tagged blue whale lunges at depth to demonstrate strong similarities, and provide rare measurements of gape size, and krill response distance and time. These findings elucidate the predator-prey relationship between blue whales and krill, and provide support for the hypothesis that surface feeding by New Zealand blue whales is an important component to their foraging ecology used to optimize their energetic efficiency. Understanding how blue whales make foraging decisions presents logistical challenges, which may cause incomplete sampling and biased ecological knowledge if portions of their foraging behavior are undocumented. We conclude that surface How to cite this article Torres LG, Barlow DR, Chandler TE, Burnett JD. 2020. Insight into the kinematics of blue whale surface foraging through drone observations and prey data. PeerJ 8:e8906 http://doi.org/10.7717/peerj.8906 foraging could be an important strategy for blue whales, and integration of UAS with tag-based studies may expand our understanding of their foraging ecology by examining surface feeding events in conjunction with behaviors at...

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The South Fork McKenzie River (SFMR) in western Oregon, USA hosts one of the largest Stage 0 stream restoration projects implemented to date. Stage 0 refers to a multichannel planform with strong hydrologic connectivity to the adjacent floodplain and surface–subsurface connectivity. Stage 0 restoration was implemented on a 900‐m‐long reach of the SFMR by re‐grading the channel and floodplain using 65,000 m3 of sediment to raise the channel bed. Thousands of large logs were added and the ends of some logs were buried in the sediment to provide foundations for future log jams. Our primary objective is to present a monitoring protocol based on randomly located sampling plots. We also analyze results from 2 years of data collection since project implementation. Within each plot, we measured canopy cover, wood volume, flow depth and velocity, organic cover (area covered by coarse and fine organic material), and substrate grain size. We used intracluster correlation coefficients and variance of measured variables to assess heterogeneity at three spatial scales: within plots, between adjacent plots, and across the entire site. Here, we evaluate changes in the first 2 years after restoration (i.e., not pre‐ vs. post‐restoration). We hypothesized that heterogeneity within a plot would decrease as the plot adjusted to local‐scale hydraulics and sediment and particulate material transport. We hypothesized that heterogeneity would increase between adjacent plots and across the entire site. We found that spatial heterogeneity of geomorphic variables decreased within plots. Heterogeneity of organic cover, sediment size, and flow depth increased between adjacent plots, although other variables did not change. Site‐scale heterogeneity decreased for all variables except organic cover and substrate. We interpret the observed geomorphic responses to reflect decreased longitudinal connectivity and increased lateral and vertical connectivity at the restoration site.

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