Expert elicitation of seasonal abundance of North Atlantic right whales Eubalaena glacialis in the mid-Atlantic

This work was supported in part by US Office of Naval Research (ONR) grants to E.F.: N00014-09-1-0896 at University of California, Santa Barbara and N00014-12-1-0274 at University of California, Davis. This work was also supported by ONR grant N000141210286 to the University of St Andrews. In addition, we gratefully acknowledge funding for this work from The Marine Alliance for Science and Technology for Scotland (MASTS). MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.North Atlantic right whales (Eubalaena glacialis; henceforth right whales) are among the most endangered large whales. Although protected since 1935, their abundance has remained low. Right whales occupy the Atlantic Ocean from southern Greenland and the Gulf of St. Lawrence south to Florida. The highly industrialized mid-Atlantic region is part of the species’ migratory corridor. Gaps in knowledge of the species’ movements through the mid-Atlantic limit informed management of stressors to the species. To contribute to filling of these gaps, we elicited estimates of the relative abundance of adult right whales in the mid-Atlantic during four months, representing each season, from ten experts. We elicited the minimum, maximum, and mode as the number of individuals in a hypothetical population of 100 right whales, and confidence estimates as percentages. For each month-sex combination, we merged the ten experts’ answers into one distribution. The estimated modes of relative abundances of both sexes were highest in January and April (females, 29 and 59; males, 22 and 23) and lowest in July and October (females, five and nine; males, three and five). In some cases, our elicitation results were consistent with the results of studies based on sightings data. However, these studies generally did not adjust for sampling effort, which was low and likely variable. Our results supplement the results of these studies and will increase the accuracy of priors in complementary Bayesian models of right whale abundances and movements through the mid-Atlantic.Publisher PDFPeer reviewe

Archiving of data on occurrence of breeding birds associated with fire treatments and controls

Since 2001, we have collected data on occupancy and relative abundance of Greater Sage- Grouse (Centrocercus urophasianus) and other species of breeding birds in the central Great Basin, and characterized the vegetation structure and composition of breeding birds’ habitats, through four projects supported by the Joint Fire Science Program (00-2-15, 01B-3-3-01, 05-2-1- 94, and 09-1-08-4). These projects collectively have generated dozens of refereed publications, dozens of invited papers or presentations, multiple M.S. theses and Ph.D. dissertations, and many workshops and field tours. Bird data included in refereed publications to date were based on point counts with a fixed radius of 75 or 100 m and a duration of 5 minutes per visit. These data previously were archived with the USDA Forest Service’s Research Data Archive. Since 2004, however, we also have conducted 100-m fixed-radius point counts with a duration of 8 minutes per visit. Furthermore, starting in 2002, we recorded birds detected beyond the fixed radius and during travel among point-count locations or at other times of day or night. We archived data on the incidental and longer-distance detections of birds, which included more than 22,600 records. We also archived all data on vegetation structure and the composition of dominant trees and shrubs collected through 2012. There are few sets of long-term, spatially extensive data on distributions and abundance of fauna or extensive characterizations of vegetation in the Great Basin. These data have considerable capacity to inform understanding and management of fire dynamics; changes in land cover, including conversion of native vegetation to cheatgrass (Bromus tectorum); and the status of species proposed for listing under the Endangered Species Act

Estimation of the occupancy of butterflies in diverse biogeographic regions

AimWe explored the extent to which occupancy of butterflies within three biogeographic regions could be explained by vegetation structure and composition, topography and other environmental attributes; whether results were consistent among regions; and whether assumptions of closure were met with assemblage-level sampling designs. LocationChesapeake Bay Lowlands (Virginia), central Great Basin (Nevada) and western Great Basin (Nevada and California) (all USA). MethodsWe applied single-season occupancy models that either assumed closure or relaxed the closure assumption to data from 2013 and 2014 for 13-15 species in each region. ResultsMaximum single-year estimates of detection probabilities ranged from 0.14 to 0.99, and single-year occupancy from 0.28 to 0.98. The assumption of closure was met for a maximum of 54% of the species in a given region and year. Detection probabilities of \u3e90% of the species in each region increased as the categorical abundance of nectar or mud increased. Measures of the dominance or abundance of deciduous woody species and structural heterogeneity were included in the greatest number of occupancy models for the Chesapeake Bay Lowlands, which may in part reflect the intensity of browsing by white-tailed deer (Odocoileus virginianus). Elevation and precipitation were prominent covariates in occupancy models for Great Basin butterflies. Main conclusionsBecause occupancy models do not rely on captures or observations of multiple individuals in a population, they potentially can be applied to a relatively high proportion of the species in an assemblage. However, estimation of occupancy is complicated by taxonomic, temporal and spatial variation in phenology. In multiple, widely divergent ecosystems, all or some associations between covariates and detection probability or occupancy for at least one-third of the species could not be estimated, often because a given species rarely was detected at locations with relatively low or high values of a covariate. Despite their advantages, occupancy models may leave unexplained the environmental associations with the distributions of many species

Silbido profundo : an open source package for the use of deep learning to detect odontocete whistles

The authors wish to thank Dr. Michael Weise of the Office of Naval Research (N00014-17-1-2867, N00014-17-1-2567) for supporting this project. We also thank Anu Kumar and Mandy Shoemaker of U.S. Navy Living Marine Resources for supporting development of the data management tools used in this work (N3943020C2202).This work presents an open-source matlab software package for exploiting recent advances in extracting tonal signals from large acoustic data sets. A whistle extraction algorithm published by Li, Liu, Palmer, Fleishman, Gillespie, Nosal, Shiu, Klinck, Cholewiak, Helble, and Roch [(2020). Proceedings of the International Joint Conference on Neural Networks, July 19–24, Glasgow, Scotland, p. 10] is incorporated into silbido, an established software package for extraction of cetacean tonal calls. The precision and recall of the new system were over 96% and nearly 80%, respectively, when applied to a whistle extraction task on a challenging two-species subset of a conference-benchmark data set. A second data set was examined to assess whether the algorithm generalized to data that were collected across different recording devices and locations. These data included 487 h of weakly labeled, towed array data collected in the Pacific Ocean on two National Oceanographic and Atmospheric Administration (NOAA) cruises. Labels for these data consisted of regions of toothed whale presence for at least 15 species that were based on visual and acoustic observations and not limited to whistles. Although the lack of per whistle-level annotations prevented measurement of precision and recall, there was strong concurrence of automatic detections and the NOAA annotations, suggesting that the algorithm generalizes well to new data.Publisher PDFPeer reviewe

Options for National Parks and Reserves for Adapting to Climate Change

Past and present climate has shaped the valued ecosystems currently protected in parks and reserves, but future climate change will redefine these conditions. Continued conservation as climate changes will require thinking differently about resource management than we have in the past; we present some logical steps and tools for doing so. Three critical tenets underpin future management plans and activities: (1) climate patterns of the past will not be the climate patterns of the future; (2) climate defines the environment and influences future trajectories of the distributions of species and their habitats; (3) specific management actions may help increase the resilience of some natural resources, but fundamental changes in species and their environment may be inevitable. Science-based management will be necessary because past experience may not serve as a guide for novel future conditions. Identifying resources and processes at risk, defining thresholds and reference conditions, and establishing monitoring and assessment programs are among the types of scientific practices needed to support a broadened portfolio of management activities. In addition to the control and hedging management strategies commonly in use today, we recommend adaptive management wherever possible. Adaptive management increases our ability to address the multiple scales at which species and processes function, and increases the speed of knowledge transfer among scientists and managers. Scenario planning provides a broad forward-thinking framework from which the most appropriate management tools can be chosen. The scope of climate change effects will require a shared vision among regional partners. Preparing for and adapting to climate change is as much a cultural and intellectual challenge as an ecological challenge

Effects of point-count duration on estimated detection probabilities and occupancy of breeding birds

Increasingly, point-count data are used to estimate occupancy, the probability that a species is present at a given location; occupancy accounts for imperfect detection, the probability that a species is detected given that it is present. To our knowledge, effects of sampling duration on inferences from models of bird occupancy have not been evaluated. Our objective was to determine whether changing count duration from 5 to 8min affected inferences about the occupancy of birds sampled in the Chesapeake Bay Lowlands (eastern United States) and the central and western Great Basin (western United States) in 2012 and 2013. We examined the proportion of species (two doves, one cuckoo, two swifts, five hummingbirds, 11 woodpeckers, and 122 passerines) for which estimates of detection probability were 0.3. For species with single-season detection probabilities 0.3, we compared occupancy estimates derived from 5- and 8-min counts. We also compared estimates for three species sampled annually for 5yr in the central Great Basin. Detection probabilities based on both the 5- and 8-min counts were 0.3 for 40% 3% of the species in an ecosystem. Extending the count duration from 5 to 8min increased the detection probability to 0.3 for 5% +/- 0.5% of the species. We found no difference in occupancy estimates that were based on 5- versus 8-min counts for species sampled over two or five consecutive years. However, for 97% of species sampled over 2yr, precision of occupancy estimates that were based on 8-min counts averaged 12% +/- 2% higher than those based on 5-min counts. We suggest that it may be worthwhile to conduct a pilot season to determine the number of locations and surveys needed to achieve detection probabilities that are sufficiently high to estimate occupancy for species of interest

Deep neural networks for automated detection of marine mammal species

Authors thank the Bureau of Ocean Energy Management for the funding of MARU deployments, Excelerate Energy Inc. for the funding of Autobuoy deployment, and Michael J. Weise of the US Office of Naval Research for support (N000141712867).Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species.Publisher PDFPeer reviewe

Improve automatic detection of animal call sequences with temporal context

Funding: This work was supported by the US Office of Naval Research (grant no. N00014-17-1-2867).Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences of calls has implications for conservation and biological studies. We show that the performance of a convolutional neural network (CNN), designed to detect song notes (calls) in short-duration audio segments, can be improved by combining it with a recurrent network designed to process sequences of learned representations from the CNN on a longer time scale. The combined system of independently trained CNN and long short-term memory (LSTM) network models exploits the temporal patterns between song notes. We demonstrate the technique using recordings of fin whale (Balaenoptera physalus) songs, which comprise patterned sequences of characteristic notes. We evaluated several variants of the CNN + LSTM network. Relative to the baseline CNN model, the CNN + LSTM models reduced performance variance, offering a 9-17% increase in area under the precision-recall curve and a 9-18% increase in peak F1-scores. These results show that the inclusion of temporal information may offer a valuable pathway for improving the automatic recognition and transcription of wildlife recordings.Publisher PDFPeer reviewe

Learning deep models from synthetic data for extracting dolphin whistle contours

We present a learning-based method for extracting whistles of toothed whales (Odontoceti) in hydrophone recordings. Our method represents audio signals as time-frequency spectrograms and decomposes each spectrogram into a set of time-frequency patches. A deep neural network learns archetypical patterns (e.g., crossings, frequency modulated sweeps) from the spectrogram patches and predicts time-frequency peaks that are associated with whistles. We also developed a comprehensive method to synthesize training samples from background environments and train the network with minimal human annotation effort. We applied the proposed learn-from-synthesis method to a subset of the public Detection, Classification, Localization, and Density Estimation (DCLDE) 2011 workshop data to extract whistle confidence maps, which we then processed with an existing contour extractor to produce whistle annotations. The F1-score of our best synthesis method was 0.158 greater than our baseline whistle extraction algorithm (~25% improvement) when applied to common dolphin (Delphinus spp.) and bottlenose dolphin (Tursiops truncatus) whistles.Postprin

Social Vulnerability of the People Exposed to Wildfires in U.S. West Coast States

Understanding of the vulnerability of populations exposed to wildfires is limited. We used an index from the U.S. Centers for Disease Control and Prevention to assess the social vulnerability of populations exposed to wildfire from 2000–2021 in California, Oregon, and Washington, which accounted for 90% of exposures in the western United States. The number of people exposed to fire from 2000–2010 to 2011–2021 increased substantially, with the largest increase, nearly 250%, for people with high social vulnerability. In Oregon and Washington, a higher percentage of exposed people were highly vulnerable (\u3e40%) than in California (~8%). Increased social vulnerability of populations in burned areas was the primary contributor to increased exposure of the highly vulnerable in California, whereas encroachment of wildfires on vulnerable populations was the primary contributor in Oregon and Washington. Our results emphasize the importance of integrating the vulnerability of at-risk populations in wildfire mitigation and adaptation plans