Knowledge about the demographic and environmental factors underlying population dynamics is fundamental to designing effective conservation measures to recover depleted wildlife populations. However, sparse monitoring data or persistent knowledge gaps about threats make it difficult to identify the drivers of population dynamics. In situations where small, declining, or depleted populations show continued evidence of decline for unknown reasons, integrated population models can make efficient use of available data to improve our understanding of demography, provide fundamental insights into factors that may be limiting recovery, and support conservation decisions. We used mark-resight and aerial survey data from 2004 to 2018 to build a Bayesian integrated population model for the Cook Inlet population of beluga whales (Delphinapterus leucas), which is listed as endangered under the U.S. Endangered Species Act. We examined the effects of prey availability and oceanographic conditions on beluga vital rates and conducted a population viability analysis to predict extinction risk across a range of hypothetical changes in beluga survival and reproduction. Our results indicated that while the survival of breeding females (0.97; 95% CI: 0.95-0.99) and young calves (0.92; 0.80-0.98) was relatively high, the survival of nonbreeders (0.94; 0.91-0.97) and fecundity (0.28; 0.22-0.36) may be depressed. Furthermore, our analysis indicates that the population will likely continue to decline, with a 17-32% probability of extinction in 150 years. Our model highlights the utility of integrated population modeling for maximizing the usefulness of available data and identifying factors contributing to the failure of protected populations to recover. This framework can be used to evaluate proposed conservation and recovery efforts for this and other endangered species. Knowledge about the demographic and environmental factors underlying population dynamics is fundamental to designing effective conservation measures to recover depleted wildlife populations. We developed a Bayesian integrated population model for the endangered Cook Inlet population of beluga whales (Delphinapterus leucas) using mark-resight and aerial survey data and used this framework to examine the effects of prey availability and oceanographic conditions on vital rates and calculate extinction risk across a range of hypothetical changes in beluga survival and reproduction. Our results indicated that survival of non-breeders and fecundity may be depressed and that the population will likely continue to decline, with a 17-32% probability of extinction in 150 years. Our model highlights the utility of integrated population modeling for maximizing the usefulness of available data, improves our understanding of demography, and could be used to identify factors contributing to the failure of this and other protected populations to recover. Photo credit: NOAA Fisheries, NMFS MMPA/ESA permit 20465.image

Bayesian estimation methods are used to fit an age- and sex-structured population model to available data on abundance and stageproportions (i.e. calves/mature animals in the population) for the Bering-Chukchi-Beaufort Seas stock of bowhead whales (Balaena mysticetus). The analyses consider three alternative population modelling approaches: (1) modelling the entire population trajectory from 1848, using the ‘backwards’ method where the trajectory is back-calculated based on assigning a prior distribution to recent abundance; (2) modelling only the recent population trajectory, using the ‘forwards from recent abundance’ method, where the population is projected forwards from a recent year and the abundance in that year is not assumed to be at carrying capacity; and (3) a version of (2) that ignores density-dependence. The ‘backwards’ method leads to more precise estimates of depletion level. In contrast, the ‘forwards from recent abundance’ method provides an alternative way of calculating catch-related quantities without having to assume that the catch record is known exactly from 1848 to the present, or having to assume that carrying capacity has not changed since 1848. Not only are all three models able to fit the abundance data well, but each is also able to remain consistent with available estimates of adult survival and age of sexual maturity. Sensitivity to the stage-proportion data and the prior distributions for the life history parameters indicates that use of the 1985 stage-proportion data has the greatest effect on the results, and that those data are less consistent with data on trends in abundance and age of sexual maturity. The analyses indicate that the population has approximately doubled in size since 1978, and the ‘backwards’ analyses suggest that the population may be approaching carrying capacity, although there is no obvious sign in the data that the population growth rate has slowed. Bayes factors are calculated to compare model fits to the data. However, there is no evidence for selecting one model over another, and furthermore, the models considered in this study result in different posterior distributions for quantities of interest to management. Posterior model probabilities are therefore calculated and used as weights to construct Bayesian model-averaged posterior distributions for outputs shared among models to take this ambiguity into account. This study represents the first attempt to explicitly quantify model uncertainty when conducting a stock assessment of bowhead whales.

ESR Endangered Species Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials ESR 18:163-167 (2012) - DOI: https://doi.org/10.3354/esr00440 NOTE Genetic analysis of right whales in the eastern North Pacific confirms severe extirpation risk R. G. LeDuc1,*, B. L. Taylor1, K. K. Martien1, K. M. Robertson1, R. L. Pitman1, J. C. Salinas2, A. M. Burdin3, A. S. Kennedy4, P. R. Wade4, P. J. Clapham4, R. L. Brownell Jr.1 1Southwest Fisheries Science Center, La Jolla, California 92037, USA 2Lab. Mamiferos Marinos, Universidad Autonoma de Baja Caifornia Sur, La Paz, BCS 23060, Mexico 3Kamchatka Branch, Pacific Institute of Geography, Russian Academy of Sciences, Petropavlovsk-Kamchatsky 683000, Russia 4National Marine Mammal Laboratory, Alaska Fisheries Science Center, Seattle, Washington 98115, USA *Email: rick.leduc@noaa.gov ABSTRACT: Genetic analysis of 49 biopsy samples from North Pacific right whales Eubalaena japonica in the eastern (48) and western (1) North Pacific revealed 24 individual whales with 7 mitochondrial haplotypes. Three pairs of large and small individuals were identified in the field; genotype analysis indicated that 2 of these could represent mother−offspring pairs; for the third small individual, no sampled female genetically qualified as a potential mother. In aggregate, the population appears to have lost some genetic diversity, though not to the degree of North Atlantic right whales E. glacialis, and males outnumber females 2:1. A comparison of the eastern Pacific samples to a single Russian sample suggested that the 2 populations are isolated to some degree. The effective population size for the eastern North Pacific was calculated to be 11.6 (95% CI: 2.9−75.0), based on the estimated linkage disequilibrium. These results further indicate that this population is at immediate risk of extirpation. KEY WORDS: Critically Endangered species · Mammal · IUCN Red List category · Eubalaena japonica · Genetics · Right whales Full text in pdf format PreviousNextCite this article as: LeDuc RG, Taylor BL, Martien KK, Robertson KM and others (2012) Genetic analysis of right whales in the eastern North Pacific confirms severe extirpation risk. Endang Species Res 18:163-167. https://doi.org/10.3354/esr00440 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in ESR Vol. 18, No. 2. Online publication date: August 16, 2012 Print ISSN: 1863-5407; Online ISSN: 1613-4796 Copyright © 2012 Inter-Research.

ESR Endangered Species Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials ESR 13:99-109 (2011) - DOI: https://doi.org/10.3354/esr00324 Rare detections of North Pacific right whales in the Gulf of Alaska, with observations of their potential prey P. R. Wade1,*, A. De Robertis1, K. R. Hough1, R. Booth2, A. Kennedy1, R. G. LeDuc3, L. Munger4, J. Napp1, K. E. W. Shelden1, S. Rankin3, O. Vasquez1, C. Wilson1 1Alaska Fisheries Science Center, National Marine Fisheries Service, 7600 Sand Point Way NE, Seattle, Washington 98115, USA 2Center for Conservation Biology, Department of Biology, University of Washington, Seattle, Washington 98195, USA 3Southwest Fisheries Science Center, National Marine Fisheries Service, 3333 N. Torrey Pines Ct, La Jolla, California 92037, USA 4Scripps Institution of Oceanography, La Jolla, California 92037, USA *Email: paul.wade@noaa.gov ABSTRACT: The North Pacific right whale Eubalaena japonica was heavily exploited throughout the Gulf of Alaska by both historical whaling and 1960s illegal Soviet catches. It is now extremely rare in this region (2 sightings between 1966 and 2003 and passive acoustic detections on 6 days out of 80 months of recordings at 7 locations). From 2004 to 2006, 4 sightings of right whales occurred in the Barnabus Trough region on Albatross Bank, south of Kodiak Island, Alaska, USA. Sightings of right whales occurred at locations within the trough with the highest density of zooplankton, as measured by active acoustic backscatter. Net trawls through a high-density demersal layer (~150 to 175 m) revealed large numbers of euphausiids and oil-rich C5-stage copepods. Photo-identification and genotyping of 2 whales failed to reveal a match to Bering Sea right whales. Fecal hormone metabolite analysis from 1 whale estimated levels consistent with an immature male, indicating either recent reproduction in the Gulf of Alaska or movements between the Bering Sea and the Gulf of Alaska. Large numbers of historic catches of right whales occurred in pelagic waters of the Gulf of Alaska, but there have been few recent detections in deep water. Given that there is no other location in the Gulf of Alaska where right whales have been repeatedly seen post-exploitation, the Barnabus Trough/Albatross Bank area represents important habitat for the relict population of North Pacific right whales in the Gulf of Alaska, and a portion of this area was designated as critical habitat under the US Endangered Species Act in 2006. KEY WORDS: North Pacific right whale · Eubalaena japonica · Prey · Gulf of Alaska · Kodiak Island · Whaling Full text in pdf format PreviousNextCite this article as: Wade PR, De Robertis A, Hough KR, Booth R and others (2011) Rare detections of North Pacific right whales in the Gulf of Alaska, with observations of their potential prey. Endang Species Res 13:99-109. https://doi.org/10.3354/esr00324 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in ESR Vol. 13, No. 2. Online publication date: January 27, 2011 Print ISSN: 1863-5407; Online ISSN: 1613-4796 Copyright © 2011 Inter-Research.

ESR Endangered Species Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials ESR 6:1-14 (2008) - DOI: https://doi.org/10.3354/esr00106 Population abundance and growth rate of western gray whales Eschrichtius robustus Amanda L. Bradford1,*, David W. Weller2, Paul R. Wade3, Alexander M. Burdin4,5,6, Robert L. Brownell Jr.7 1School of Aquatic and Fishery Sciences, University of Washington, Box 355020, Seattle, Washington 98195-5020, USA 2Southwest Fisheries Science Center, NMFS, NOAA, 8604 La Jolla Shores Drive, La Jolla, California 92037-0271, USA 3National Marine Mammal Laboratory, Alaska Fisheries Science Center, NMFS, NOAA, 7600 Sand Point Way NE, Seattle, Washington 98115-6349, USA 4Kamchatka Branch of Pacific Institute of Geography, Far East Branch of the Russian Academy of Sciences, Pr. Rybakov, 19-a, Petropavlovsk-Kamchtsky 683024, Russia 5Alaska SeaLife Center, 301 Railway Avenue, Seward, Alaska 99664, USA 6University of Alaska Fairbanks, PO Box 757500, Fairbanks, Alaska 99775, USA 7Southwest Fisheries Science Center, NMFS, NOAA, 1352 Lighthouse Avenue, Pacific Grove, California 93950, USA *Email: alb992@u.washington.edu ABSTRACT: The western population of gray whales Eschrichtius robustus is one of the most endangered whale populations in the world. Recent studies of this population off the northeastern coast of Sakhalin Island, Russia, have produced a photographic dataset that was utilized for the first mark-recapture assessment of western gray whale abundance. Given encounter histories of 129 individually identified whales spanning 25 monthly capture occasions from 1997 to 2003, a closed capture estimator was employed to estimate the number of individuals using the study area in each year. Temporary emigration probabilities were then applied to the closed capture estimates to enumerate the total population size of whales off northeastern Sakhalin Island. Total abundances from 1997 to 2003 were estimated as 64 ± 5.1 (SE), 55 to 75 (95% CI); 75 ± 4.9, 66 to 85; 86 ± 3.1, 80 to 93; 77 ± 4.7, 68 to 87; 91 ± 3.4, 84 to 98; 98 ± 4.1, 90 to 106; and 99 ± 4.9, 90 to 109, respectively. These abundance estimates, particularly the last values in the series, most likely approximate the size of the entire western gray whale population. For comparison to the trend in the abundance estimates, life history data were used to estimate the growth rate of the population. Depending on the range of potential fecundity values incorporated, the resulting growth rate estimates indicate an annual population increase that is between 2.5 and 3.2%. The extremely small population size and slow rate of increase documented here further highlight concern about the viability of this critically endangered population. KEY WORDS: Abundance · Mark-recapture · Temporary emigration · Population growth rate · Simulation approach · Photo-identification · Western gray whale · Sakhalin Island, Russia Full text in pdf format NextCite this article as: Bradford AL, Weller DW, Wade PR, Burdin AM, Brownell RL Jr. (2008) Population abundance and growth rate of western gray whales Eschrichtius robustus. Endang Species Res 6:1-14. https://doi.org/10.3354/esr00106 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in ESR Vol. 6, No. 1. Online publication date: September 09, 2008 Print ISSN: 1863-5407; Online ISSN: 1613-4796 Copyright © 2008 Inter-Research.

We estimated potential limits to anthropogenic mortality for harbour porpoises in the Baltic region (the Skagerrak, Kattegat, Great Belt and Little Belt Seas, the Kiel and Mecklenburg Bights, and the Baltic Sea) using conservation objectives set by the Agreement on the Conservation of Small Cetaceans in the Baltic and North Seas (ASCOBANS). Mortality limits (ML) were calculated as the product of: a minimum estimate of abundance, one-half the maximum rate of increase and an uncertainty factor. Previous models show that if anthropogenic mortality is less than ML, a depleted population should recover to more than 80% of carrying capacity, meeting the conservation objectives of ASCOBANS. Minimum estimates of by-catches exceed ML for the population structure hypothesis tested, indicating that these catches will impede recovery. The same result was also evident for other hypothetical population structures. We conclude that immediate management actions are necessary to reduce the magnitude of by-catches to meet the conservation objectives of ASCOBANS.

Abstract: Good management models and good models for understanding biology differ in basic philosophy. Management models must facilitate management decisions despite large amounts of uncertainty about the managed populations. Such models must be based on parameters that can be estimated readily, must explicitly account for uncertainty, and should be simple to understand and implement. In contrast, biological models are designed to elucidate the workings of biology and should not be constrained by management concerns. We illustrate the need to incorporate uncertainty in management models by reviewing the inadequacy of using standard biological models to manage marine mammals in the United States. Past management was based on a simple model that, although it may have represented population dynamics adequately, failed as a management tool because the parameter that triggered management action was extremely difficult to estimate for the majority of populations. Uncertainty in parameter estimation resulted in few conservation actions. We describe a recently adopted management scheme that incorporates uncertainty and its resulting implementation. The approach used in this simple management scheme, which was tested by using simulation models, incorporates uncertainty and mandates monitoring abundance and human‐caused mortality. Although the entire scheme may be suitable for application to some terrestrial and marine problems, two features are broadly applicable: the incorporation of uncertainty through simulations of management and the use of quantitative management criteria to translate verbal objectives into levels of acceptable risk.

Abstract: Bayesian statistical inference provides an alternate way to analyze data that is likely to be more appropriate to conservation biology problems than traditional statistical methods. I contrast Bayesian techniques with traditional hypothesis‐testing techniques using examples applicable to conservation. I use a trend analysis of two hypothetical populations to illustrate how easy it is to understand Bayesian results, which are given in terms of probability. Bayesian trend analysis indicated that the two populations had very different chances of declining at biologically important rates. For example, the probability that the first population was declining faster than 5% per year was 0.00, compared to a probability of 0.86 for the second population. The Bayesian results appropriately identified which population was of greater conservation concern. The Bayesian results contrast with those obtained with traditional hypothesis testing. Hypothesis testing indicated that the first population, which the Bayesian analysis indicated had no chance of declining at >5% per year, was declining significantly because it was declining at a slow rate and the abundance estimates were precise. Despite the high probability that the second population was experiencing a serious decline, hypothesis testing failed to reject the null hypothesis of no decline because the abundance estimates were imprecise. Finally, I extended the trend analysis to illustrate Bayesian decision theory, which allows for choice between more than two decisions and allows explicit specification of the consequences of various errors. The Bayesian results again differed from the traditional results: the decision analysis led to the conclusion that the first population was declining slowly and the second population was declining rapidly.

Surveys to determine the abundance of marine mammals are expensive, and it is therefore advisable to use an objective process for planning the frequency and intensity of surveys. Previous authors have addressed methods for designing surveys such that 1) a particular level of precision is achieved in single or multiple surveys or 2) a specified trend in abundance is detected with a given probability and number of surveys. We propose an alternative method to consider in designing a series of surveys aimed at minimizing the probability of incorrectly classifying a stock relative to management goals. An example of the above is the classification of stocks as "strategic" under the U.S. Marine Mammal Protection Act. To address this question a series of simulations were performed, where the underlying population level and level of human-related mortality were specified. The effect of survey interval on the rate of incorrectly classifying a stock was examined for a range of precision levels for abundance and human-caused mortality estimates. Four case studies were used to examine the effect of survey interval in more detail. In general, coefficients of variation of the abundance estimates of less than 0.5 were necessary to achieve error rates of less than 0.1, unless the estimates of human-caused mortality were precise (<0.3). Recommended survey intervals between 1 year and 8 years (i.e., the predetermined maximum interval in the analysis) depend upon the level of precision that had been achieved in previous estimates of abundance and human-caused mortality. In addition, averaging abundance and mortality estimates over specified time periods substantially reduced the rate of mis-classifying a fishery.

AbstractA simulation method was developed for identifying populations with levels of human‐caused mortality that could lead to depletion, taking into account the uncertainty of available information. A mortality limit (termed the Potential Biological Removal, PBR, under the U. S. Marine Mammal Protection Act) was calculated as the product of a minimum population estimate (NMIN), one‐half of the maximum net productivity rate (RMAX), and a recovery factor (FR). Mortality limits were evaluated based on whether at least 95% of the simulated populations met two criteria: (1) that populations starting at the maximum net productivity level (MNPL) stayed there or above after 20 yr, and (2) that populations starting at 30% of carrying‐capacity (K) recovered to at least MNPL after 100 yr. Simulations of populations that experienced mortality equal to the PBR indicated that using approximately the 20th percentile (the lower 60% log‐normal confidence limit) of the abundance estimate for NMIN met the criteria for both cetaceans (assuming RMAX= 0.04) and pinnipeds (assuming RMAX= 0.12). Additional simulations that included plausible levels of bias in the available information indicated that using a value of 0.5 for FR would meet both criteria during these “bias trials.” It is concluded that any marine mammal population with an estimate of human‐caused mortality that is greater than its PBR has a level of mortality that could lead to the depletion of the population. The simulation methods were also used to show how mortality limits could be calculated to meet conservation goals other than the U. S. goal of maintaining populations above MNPL.

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon‐Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under‐ or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

Dolphins (Delpkinidae) have been killed incidentally by the purse-seine fishery for yellowfin tuna, Thunnus albacares, in the eastern tropical Pacific since at least 1959. Annual estimates of the number of dolphins killed from each stock are used by the National Marine Fisheries Service in making management decisions about the population status of affected stocks. Mortality estimates from the period with the greatest kill of dolphins, 1959-72, are important for estimates of the level of depletion of these stocks from their unexploited population sizes. A redefinition of the geographical boundaries of offshore stocks of pantropical spotted dolphins, Stenella attenuata, makes it necessary to estimate annual kill for these newly defined stocks for 1959-72. I estimated the number of dolphins killed annually from 1959 to 1972 for the northeastern and western/southern stocks of spotted dolphins, using the methods of Lo and Smith (1986). I also revised the estimates of annual kill for the eastern and whitebelly stocks of spinner dolphins, S. longirostris, by correcting minor problems in previous data and analyses. Additionally, I estimated a coefficient of variation (CV) for each stock-specific estimate of incidental kill, which had not previously been done. Estimates of total kill were similar to previous estimates: 4.9 million dolphins are estimated to have been killed by the purse-seine fishery over the fourteen year period considered here, an average of 347,082 per year. Nearly all of the fisheries kill of pantropical spotted dolphins was of the northeastern stock, totaling 3.0 million (211,612 per year). Estimates of kill for the eastern stock of spinner dolphins were similar to previous estimates, totaling 1.3 million (91,739 per year). As expected, CTPs of the kill for each stock were higher than those previously reported for the total kill.