Predator-Mediated Indirect Effects of Snowshoe Hares on Dall's Sheep in Alaska

ABSTRACT Indirect interactions among species can strongly influence population dynamics and community structure but are often overlooked in management of large mammals. We estimated survival of Dall's sheep (Ovis dalli) in the central Alaska Range, USA, during years of differing snowshoe hare (Lepus americanus) abundance to test whether indirect interactions with a cyclic hare population affect Dall's sheep either negatively, by subsidizing predators (apparent competition), or positively, by diverting predation (apparent commensalism). Annual survival of adult female sheep was consistently high (0.85 for all yr and age classes combined). In contrast, annual estimates of lamb survival ranged from 0.15 to 0.63. The main predators of lambs were coyotes (Canis latrans) and golden eagles (Aquila chrysaetos), which rely on hares as their primary food and prey on lambs secondarily. Coyotes and eagles killed 78% of 65 radiocollared lambs for which cause of death was known. Lamb survival was negatively related to hare abundance during the previous year, and lamb survival rates more than doubled when hare abundance declined, supporting the hypothesis of predator-mediated apparent competition between hares and sheep. However, stage-specific predation and delays in predator responses to changes in hare numbers led to a positive relationship between abundance of adult Dall's sheep and hares. Lacking reliable estimates of survival, a manager might erroneously conclude that hares benefit sheep. Thus, support for different indirect effects can be obtained from different types of data, which demonstrates the need to determine the mechanisms that create indirect interactions. Long-term survey data suggest that predation by coyotes is limiting this sheep population below levels typical when coyotes were rare or absent. Understanding the nature of indirect interactions is necessary to effectively manage complex predator–prey communities.     

Challenges and opportunities for estimating abundance of a low-density moose population

Monitoring large herbivores across their core range has been readily accomplished using aerial surveys and traditional distance sampling. But for peripheral populations, where individuals may occur in patchy, low-density populations, precise estimation of population size and trend remains logistically and statistically challenging. For moose (Alces alces) along their southern range margin in northern New York, USA, we sought robust estimates of moose distribution, abundance, and population trend (2016–2019) using a combination of aerial surveys (line transect distance-sampling), repeated surveys in areas where moose were known to occur to boost the number of detections, and density surface modeling (DSM) with spatial covariates. We achieved a precise estimate of density (95% CI = 0.00–0.29 moose/km²) for this small population (656 moose, 95% CI = 501–859), which was patchily distributed across a large and heavily forested region (the 24,280-km² Adirondack Park). Local moose abundance was positively related to active timber management, elevation, and snow cover, and negatively related to large bodies of water. As expected, moose abundance in this peripheral population was low relative to its core range in other northern forest states. Yet, in areas where abundance was greatest, moose densities in New York approached those where epizootics of winter tick (Dermacentor albipictus) have been reported, underscoring the need for effective and efficient monitoring. By incorporating autocorrelation in observations and landscape covariates, DSM provided spatially explicit estimates of moose density with greater precision and no additional field effort over traditional distance sampling. Combined with repeated surveys of areas with known moose occurrence to achieve viable sample sizes, DSM is a useful tool for effectively monitoring low density and patchy populations.     

Comparing contemporary models to traditional indices to estimate abundance of desert bighorn sheep

Aerial surveys for large ungulates produce count data that often underrepresent the number of animals. Errors in count data can lead to erroneous estimates of abundance if they are not addressed. Our objective was to address imperfect detection probability by developing a framework that produces realistic and defensible estimates of bighorn sheep (Ovis canadensis) abundance. We applied our framework to a population of desert bighorn sheep (O. c. nelsoni) in the Great Basin, Nevada, USA. We captured and marked 24 desert bighorn sheep with global positioning system (GPS)-collars and then conducted helicopter surveys naïve to the locations of collared animals. We developed a Bayesian integrated data model to leverage information from telemetry data, helicopter survey counts, and habitat characteristics to estimate abundance while accounting for availability and perception probability (i.e., detection given availability). Distance to ridgeline, terrain ruggedness, tree cover, and slope influenced perception probability of sheep given they were viewable from the helicopter. There was also annual variation in perception probability (2018: median = 0.64, credible interval [CrI] = 0.37–0.87; 2019: median = 0.81, CrI = 0.49–0.97). The abundance estimates from the integrated data model decreased from 2018 (594; 95% CrI = 537–656) to 2019 (487; 95% CrI = 436–551). In addition, accounting for availability and imperfect perception resulted in greater estimates of abundance compared to traditional directed search methods, which were 340 for 2018 and 320 for 2019. Our modeling framework can be used to generate more defensible population estimates of bighorn sheep and other large mammals that have been surveyed in a similar manner.     

Using distance sampling techniques to estimate bottlenose dolphin (Tursiops truncatus) abundance at Turneffe Atoll,Belize

Reliable abundance estimates are critical for management and conservation of coastal small cetaceans. This is particularly important in developing countries where coastal human populations are increasing, the impacts of anthropogenic activities are often unknown, and the resources necessary to assess coastal cetaceans are limited. We adapted ship‐based line transect methods to small‐boat surveys to estimate the abundance of bottlenose dolphins (Tursiops truncatus) at Turneffe Atoll, Belize. Using a systematic survey design with random start and uniform coverage, 34 dolphin clusters were sighted during small‐boat line transect surveys conducted in 2005–2006. Distance sampling methods estimated abundance at 216 individuals (CV = 27.7%, 95% CI = 126–370). Due to species rarity in the Atoll, small sample size, and potential violations in line transect assumptions, the estimate should be considered preliminary. Nevertheless, it provides up‐to‐date information on the status of a regional population in an area under increasing threat of habitat loss and prey depletion via uncontrolled development and unsustainable fishing. This information will be useful as Belize develops a new conservation initiative to create a comprehensive and resilient marine protected area system. Our study illustrates the application of distance sampling methods to small‐boat surveys to obtain abundance estimates of coastal cetaceans in a region lacking resources.     

A noninvasive and integrative approach for improving density and abundance estimates of moose

Acquiring demographic data for moose (Alces alces) can be difficult because they are solitary in nature, they prefer densely vegetated and mountainous habitats, and they often occur at low density. Such data, however, are essential for long-term population monitoring, evaluating management practices, and effective conservation. Winter aerial surveys are the standard method for estimating moose population parameters, but they can be logistically challenging, expensive, and subject to sightability correction, which necessitates the capture of study animals for initial model development. Herein, we demonstrate a noninvasive alternative approach for estimating population parameters of moose in northern Yellowstone National Park, where aerial surveys were attempted but proved ineffective. We determined individual moose genotype and sex using microsatellite polymerase chain reaction amplification of DNA extracted from fecal pellets, integrated ancillary pellet sample data (i.e., metadata) in genotype analysis to aid in the identification of matching genotypes, and used spatially explicit capture-recapture (SECR) modeling to estimate sex-specific density and abundance. We collected 616 samples over 3 consecutive winters (Dec 2013–Apr 2016) and within 2 sampling occasions each winter. We recorded 514 captures of 142 individual moose (69 males, 73 females). Overall density ranged between 0.062 moose/km² and 0.076 moose/km² and averaged 0.034/km² for females and 0.033/km² for males. Abundance estimates were 150 moose in 2013 (female = 76, 95% CI = 55–105; male = 74, 95% CI = 54–103), 186 in 2014 (female = 95, 95% CI = 63–142; male = 91, 95% CI = 60–138), and 160 in 2015 (female = 79, 95% CI = 58–108; male = 81, 95% CI = 59–110). Average population sex ratio was 0.99 males/female. We demonstrate that SECR analysis of fecal DNA genotypes, using metadata in genotype analysis to help identify matching moose genotypes, is a promising alternative method for estimating sex-specific density and abundance of a low-density moose population in a mountainous and forested landscape.     

Integrating distance sampling with minimum counts to improve monitoring

Minimum counts are commonly used to estimate population size and trend for wildlife conservation and management; however, the scope of inference based on such data is limited by untestable assumptions regarding the detection process. Alternative approaches, such as distance sampling, occupancy surveys, and repeated counts, can be employed to produce detection-corrected estimates of population parameters. Unfortunately, these approaches can be more complicated and costly to implement, potentially limiting their use. We explored a conceptual framework linking datasets collected at different spatial scales under different survey designs, with the goal of improving inference. Specifically, we link landscape-scale distance sampling surveys with local-scale minimum counts in an integrated modeling framework to estimate mountain goat (Oreamnos americanus) abundance at both the local and regional scale in south-central Alaska, USA, and provide an estimate of detection probability (i.e., sightability) for the minimum counts. Estimated sightability for the minimum count surveys was 0.67 (95% credible interval [CrI] = 0.52–0.83) and abundance for the entire area was 5,600 goats (CV = 9%), both in broad agreement with estimates from previous studies. Abundance estimates at the local scale (i.e., individual min. count unit) were reasonably precise ( = 18%), suggesting the integrated approach can increase the amount of information produced at both spatial scales by linking minimum count approaches with more rigorous survey designs. We propose that our integrated approach may be implemented in the context of a modified split-panel monitoring design by altering survey protocols to include frequent minimum counts within local count units and intermittent but more rigorous survey designs with inference to the entire study area or population of interest. Doing so would provide estimates of abundance with appropriate measures of uncertainty at multiple spatial scales, thereby improving inference for population monitoring and management. © 2019 The Wildlife Society.     

Evaluating the current condition of a threatened marine mammal population: Estimating northern sea otter (Enhydra lutris kenyoni) abundance in southwest Alaska

We estimated density and abundance of the threatened southwest Alaska distinct population segment of northern sea otters (Enhydra lutris kenyoni) in two management units. We conducted aerial surveys in Bristol Bay and South Alaska Peninsula management units in 2016, and modeled sea otter density and abundance with Bayesian hierarchical distance sampling models and spatial environmental covariates (depth, distance to shore, depth × distance to shore). Spatial environmental covariates substantially impacted sea otter group density in both management units, but effects sizes differed between the two management units. Abundance (9,733 otters, 95% CrI 6,412–17,819) and density (0.82 otters/km², 95% CrI 0.54–1.49) estimates for Bristol Bay indicated a moderate population size. In contrast, abundance (546 otters, 95% CrI 322–879) and density (0.06 otters/km², 95% CrI 0.03–0.09) estimates indicated a relatively low population size in South Alaska Peninsula. Overall, our results highlight the importance of accounting for the detection process in monitoring at-risk species to reduce the uncertainty associated with making conclusions about population declines.     

Reproductive,behavioral, and endocrine characteristics of the Dall's sheep (Ovis dalli dalli)

Failed interspecific embryo transfer between Dall's sheep (Ovis dalli dalli) and domestic ewes (Ovis aries), and a paucity of physiological data available for the Dall's sheep, provided incentive for developing a reproductive database in Dall's sheep. Urine samples were collected on a regular basis from a captive herd of Dall's sheep during the breeding to lambing interval over a 3 year period. To provide comparative endocrine data during pregnancy, samples also were collected during a single gestation period from five Suffolk ewes. All urine samples were analyzed for pregnanediol-3-glucuronide (PdG) and estrone conjugates (EC). Behavioral observations from Dall's sheep were made each year during the rut and lambing periods to calculate estrous cycle and gestation lengths. Placentas from Dall's sheep (n = 20) and Suffolk ewes (n = 10) were collected, the total number of cotyledons counted, and the length, width, area, and weight of eight cotyledons/horn from each placenta calculated. Endocrine and behavioral results indicated that the Dall's sheep is seasonally polyestrous and monovulatory, with a mean estrous cycle length of 18.2 ± 0.3 days, a mean luteal phase of 11.8 ± 0.5 days, and an average gestation length of 171.6 ± 0.9 days. While hormonal patterns for pregnancy generally were similar between the two species, there was a pronounced difference in the magnitude of hormone concentrations, particularly PdG, and an absence of a marked preparturition EC rise in domestic sheep. Cotyledon numbers, weight, and area were smaller (P < 0.05) in the Dall's sheep, compared to Suffolk ewes. These data suggest that unsuccessful interspecies embryo transfer attempts may have been due to failed fetal/maternal communications resulting from hormonal and/or placental differences. © 1996 Wiley-Liss, Inc.     

Evaluating abundance estimates and evidence of breeding for Bobolinks from transect and point-count surveys

Estimating the abundance and breeding success of territorial songbirds is challenging. Various types of surveys and analyses are available, but all receive some criticism in the literature, and most methods are rarely compared with results obtained using intensive monitoring efforts. We assessed the efficacy of transect and point-count surveys to estimate the abundance of male Bobolinks (Dolichonyx oryzivorus) and detect evidence of nesting and fledging by comparing the results of those surveys to results from more intensive monitoring (i.e., spot mapping and nest monitoring). We monitored 36 fields (254 ha) of late-harvest hay, restored grassland, and fallow fields in the Luther Marsh Wildlife Management Area and on four farms in southern Ontario, Canada, in 2018. Compared to the number of territories identified based on spot mapping (197), distance sampling analysis of transect survey data provided a more accurate estimate of the abundance of male Bobolinks (230, 95% CI: 187, 282) than N-mixture models of transect (668, 95% CI: 332, 1342) and point-count (337, 95% CI: 203, 559) data. Three visits to survey transects and five to point counts did not effectively detect evidence of Bobolink breeding (i.e., nesting or fledging) in fields compared to spot mapping and nest monitoring. Distance sampling analysis of transect data appears promising for estimating the number of Bobolink territories in an area, e.g., those impacted by conservation programs. If estimates of the number of nesting Bobolinks and frequency of fledging are of interest, spot mapping and nest monitoring could be implemented at a subset of sampled fields. Our results suggest that additional studies to evaluate model-based estimates of abundance with the best available information (e.g., from spot mapping of marked or unmarked populations and nest monitoring) would be useful to ensure that robust estimates are provided to support population estimates and conservation actions.     

Environmental Influences on Dall's Sheep Survival

Understanding how species respond to environmental conditions can assist with conservation strategies and harvest management, especially in arctic and boreal regions that are experiencing rapid climate change. Although climatic influences on species distributions have been studied, broad-scale effects of climate on survival are less well known. We examined the interactive effects of meteorological and remotely sensed environmental variables on survival of Dall's sheep (Ovis dalli dalli) lambs and adults by synthesizing radio-telemetry data across their range. We used data from 9 studies of adult sheep and 2 studies of lambs that were conducted between 1997 and 2012 at sites spanning the species' range in Alaska, USA, and northwestern Canada. We obtained environmental variables throughout the range of Dall's sheep, including the normalized difference vegetation index (NDVI) from optical remote sensing, freeze-thaw frequency (FTF) from passive microwave remote sensing, and gridded climate variables such as snow water equivalent, temperature, and precipitation. We used Cox proportional hazard regression to investigate the effects of environmental variables recorded during summer, winter, and the previous winter on annual survival rates of Dall's sheep lambs and adults. Summer NDVI was the most influential environmental factor affecting lamb survival, with improved lamb survival occurring in years with a high maximum NDVI. Also, lamb predation by coyotes (Canis latrans) and golden eagles (Aquila chrysaetos) decreased substantially with increasing NDVI. The previous winter FTF had the strongest effect on adult survival, with decreased survival occurring after winters with high FTF. In addition, these remotely sensed environmental factors interacted with meteorological factors to affect survival, such that effects of winter temperature depended on summer NDVI and winter FTF. Warm winters increased lamb survival only when preceded by summers with high NDVI, and warm winters increased adult survival only when winter FTF was low. Thus, potential benefits of climate warming may be counteracted if wintertime freeze-thaw events markedly increase. Correlations among environmental variables across sites were low, and regional climate cycles such as the Pacific Decadal Oscillation (PDO) had weak effects, indicating substantial local variability in climatic conditions experienced by Dall's sheep across their range. These findings can help managers anticipate how Dall's sheep populations will respond to changes in local environmental conditions. Our results also highlight the utility of multiple remotely sensed environmental conditions for ungulate management, especially passive microwave products that provide valuable information on winter icing events. © 2020 The Wildlife Society.     

CORRECTING AERIAL SURVEY COUNTS OF HARBOR SEALS (PHOCA VITULINA RICHARDSI) IN WASHINGTON AND OREGON

Aerial surveys of harbor seals on land produce only a minimum assessment of the population; a correction factor to account for the missing animals is necessary to estimate total abundance. In 1991 and 1992, VHF radio tags were deployed on harbor seals ( n = 124) at six sites in Washington and Oregon. During aerial surveys a correction factor to account for seals in the water was determined from the proportion of radio-tagged seals on shore during the pupping season. This proportion ranged from 0.54 to 0.74. Among the six sites there was no significant difference in the proportion of animals on shore nor was there a difference in age/sex categories of seals on shore between sites. The pooled correction factor for determining total population abundance was 1.53. An additional 32 seals were radio tagged in 1993 at one of the sites used in 1991. Comparing data from the two years, we found no interannual variation. Aerial surveys of all known harbor seal haul-out sites in Washington ( n = 319) and Oregon ( n = 68) were flown during the peak of the pupping season, 1991–1993. The Washington and Oregon harbor seal population was divided into two stocks based on pupping phenology, morphometics, and genetics. Mean counts for the Washington inland stock were 8,710 in 1991, 9,018 in 1992, and 10,092 in 1993. Oregon and Washington coastal stock mean counts were 18,363 in 1991, 18,556 in 1992, and 17,762 in 1993. Multiplying the annual count by the correction factor yielded estimates of harbor seal abundance in the Washington inland stock of 13,326 (95% CI = 11,637–15,259) for 1991, 13,798 (95% CI = 11,980–15,890) for 1992, and 15,440 (95% CI = 13,382–17,814) for 1993. In the Oregon and Washington coastal stock the corrected estimate of harbor seal abundance was 28,094 (95% CI = 24,697–31,960) in 1991, 28,391 (95% CI = 24,847–32,440) for 1992, and 27,175 (95% CI = 23,879–30,926) for 1993.     

Temporal–spatial distribution of an island‐based offshore population of common bottlenose dolphins (Tursiops truncatus) in the equatorial Atlantic

A little‐studied common bottlenose dolphin (Tursiops truncatus) population inhabits the offshore waters surrounding Saint Paul's Rocks, a Brazilian marine protected area in the equatorial Atlantic Ocean. Five field expeditions (May 2011–May 2013) were conducted to characterize the habitat use, population size, and site fidelity of this population. Three different survey methods were employed: line‐transect surveys, land‐based surveys, and photo‐identification surveys. A population size of 23 individuals (19–28, CI 95%), which were present on most sampling days (>90% of surveys), was estimated. The maximum resighting interval of photo‐identified animals was 9 yr and 3 mo for five distinct individuals, based on data from nonsystematic efforts that have been ongoing since 2004. The dolphins exhibited strong site fidelity, as the minimum convex polygon (MCP, 95%) method revealed that they restricted their movements to a 0.5 km² area across seasons and a 0.99 km² area across years (95% kernel). The dolphins preferred shallow waters close to the archipelago (<1.2 km from the islands), especially on the eastern and southeastern sides, where oceanographic models have revealed persistent upwelling that may result from underwater currents and where food may be more predictably available.     

Detection probability in aerial surveys of feral horses

Observation bias pervades data collected during aerial surveys of large animals, and although some sources can be mitigated with informed planning, others must be addressed using valid sampling techniques that carefully model detection probability. Nonetheless, aerial surveys are frequently employed to count large mammals without applying such methods to account for heterogeneity in visibility of animal groups on the landscape. This often leaves managers and interest groups at odds over decisions that are not adequately informed. I analyzed detection of feral horse (Equus caballus) groups by dual independent observers from 24 fixed-wing and 16 helicopter flights using mixed-effect logistic regression models to investigate potential sources of observation bias. I accounted for observer skill, population location, and aircraft type in the model structure and analyzed the effects of group size, sun effect (position related to observer), vegetation type, topography, cloud cover, percent snow cover, and observer fatigue on detection of horse groups. The most important model-averaged effects for both fixed-wing and helicopter surveys included group size (fixed-wing: odds ratio = 0.891, 95% CI = 0.850–0.935; helicopter: odds ratio = 0.640, 95% CI = 0.587–0.698) and sun effect (fixed-wing: odds ratio = 0.632, 95% CI = 0.350–1.141; helicopter: odds ratio = 0.194, 95% CI = 0.080–0.470). Observer fatigue was also an important effect in the best model for helicopter surveys, with detection probability declining after 3 hr of survey time (odds ratio = 0.278, 95% CI = 0.144–0.537). Biases arising from sun effect and observer fatigue can be mitigated by pre-flight survey design. Other sources of bias, such as those arising from group size, topography, and vegetation can only be addressed by employing valid sampling techniques such as double sampling, mark–resight (batch-marked animals), mark–recapture (uniquely marked and identifiable animals), sightability bias correction models, and line transect distance sampling; however, some of these techniques may still only partially correct for negative observation biases. © 2011 The Wildlife Society.     

Bayesian spatial modeling of moose count data: increasing estimator efficiency and exploring ecological hypotheses

Moose management throughout much of Alaska and Canada relies on aerial count data, which are commonly collected using the geospatial population estimator (GSPE) protocol. The GSPE uses a model-based analytical approach and finite-population block kriging to estimate abundance from a collection of sampled survey units. Widespread implementation and well-documented analytical software have resulted in reliable estimates of moose abundance, density, and composition across a large proportion of their range. Analysis is conducted almost exclusively using the GSPE software, which fits a fixed model structure to data collected within a single year. The downside of this approach to analysis is that the fixed model structure is inefficient for estimation, leading to more field effort than would otherwise be necessary to achieve a desired level of estimator precision. We developed a more easily modified and flexible Bayesian spatial general additive model approach (BSG) that accommodates spatial and temporal covariates (e.g., habitat characteristics, trend), multiple survey events, prior information, and incomplete detection. Using a series of 6 GSPE surveys conducted in Yukon-Charley Rivers National Preserve, Alaska, USA, from 2003–2019, we established the equivalence of the 2 approaches under similar model structures. We then extended the BSG to demonstrate how a more comprehensive approach to analysis can affect estimator precision and be used to assess ecological relationships. The precision of annual abundance estimators from the BSG were improved by an average of 43% over those based on the standard GSPE analysis, highlighting the very real costs of assuming a fixed (i.e., suboptimal) model structure. The population increased at a rate of 2.3%/year (95% CrI = 0.8–3.8%), and the increase was largely explained by a parallel increase in wildfire extent (i.e., high quality moose habitat). These results suggest that our approach could be used to increase estimator efficiency or decrease future survey costs without any modifications to the basic protocol. While modification of the GSPE software is possible, practitioners may find the BSG approach more convenient for quickly developing model structures for a particular application, thereby allowing them to extract more information from existing and future datasets.     

Estimating breeding season abundance of golden-cheeked warblers in Texas,USA

Population abundance estimates using predictive models are important for describing habitat use and responses to population-level impacts, evaluating conservation status of a species, and for establishing monitoring programs. The golden-cheeked warbler (Setophaga chrysoparia) is a neotropical migratory bird that was listed as federally endangered in 1990 because of threats related to loss and fragmentation of its woodland habitat. Since listing, abundance estimates for the species have mainly relied on localized population studies on public lands and qualitative-based methods. Our goal was to estimate breeding population size of male warblers using a predictive model based on metrics for patches of woodland habitat throughout the species' breeding range. We first conducted occupancy surveys to determine range-wide distribution. We then conducted standard point-count surveys on a subset of the initial sampling locations to estimate density of males. Mean observed patch-specific density was 0.23 males/ha (95% CI = 0.197–0.252, n = 301). We modeled the relationship between patch-specific density of males and woodland patch characteristics (size and landscape composition) and predicted patch occupancy. The probability of patch occupancy, derived from a model that used patch size and landscape composition as predictor variables while addressing effects of spatial relatedness, best predicted patch-specific density. We predicted patch-specific densities as a function of occupancy probability and estimated abundance of male warblers across 63,616 woodland patches accounting for 1.678 million ha of potential warbler habitat. Using a Monte Carlo simulation, our approach yielded a range-wide male warbler population estimate of 263,339 (95% CI: 223,927–302,620). Our results provide the first abundance estimate using habitat and count data from a sampling design focused on range-wide inference. Managers can use the resulting model as a tool to support conservation planning and guide recovery efforts. © 2012 The Wildlife Society.     

Estimating the population size of the Sanje mangabey (Cercocebus sanjei) using acoustic distance sampling

The Sanje mangabey (Cercocebus sanjei) is endemic to the Udzungwa Mountains, Tanzania, and is classified as Endangered due to its putatively declining population size, habitat degradation and fragmentation. Previous population size estimates have ranged from 1,350 to 3,500 individuals, with the last direct survey being conducted 15 years before the present study. Previous estimates are now thought to have underestimated the population due to a limited knowledge of group and habitat size, nonsystematic approaches and the use of visual methods that are not suitable for surveying the Sanje mangabey with its semi-terrestrial and elusive behaviors. We used an acoustic survey method with observers recording the distinctive “whoop-gobble” vocalization produced by mangabeys and point transect distance sampling to model a detection function and estimate abundance. Twenty-eight surveys were conducted throughout the two forests where Sanje mangabeys are found: Mwanihana forest in the Udzungwa Mountains National Park (n = 13), and the Uzungwa Scarp Nature Reserve (n = 15). Group density was found to be significantly lower in the relatively unprotected Uzungwa Scarp forest (0.15 groups/km²; 95% CI: 0.08–0.27) compared to the well-protected Mwanihana forest (0.29 groups/km²; 95% CI: 0.19–0.43; p = .03). We estimate that there are 1,712 (95% CI: 1,141–2,567) individuals in Mwanihana and 1,455 (95% CI: 783–2,702) in the Uzungwa Scarp, resulting in a total population size of 3,167 (95% CI: 2,181–4,596) individuals. The difference in group density between sites is likely a result of the differing protection status and levels of enforcement between the forests, suggesting that protection of the Uzungwa Scarp should be increased to encourage recovery of the population, and reduce the threat of degradation and hunting. Our results contribute to the reassessment of the species' IUCN Red List status and informing management and conservation action planning.     

Mark‐resight abundance and survival estimation of Indo‐Pacific bottlenose dolphins,Tursiops aduncus,in the Swatch‐of‐No‐Ground,Bangladesh

A mark‐resight analysis under Pollock's robust design was applied to Indo‐Pacific bottlenose dolphins Tursiops aduncus in the Swatch‐of‐No‐Ground (SoNG) submarine canyon, Bangladesh, during the winter seasons of 2005–2009. Information from sightings of photo‐identified individuals (1,144) and unmarked individuals generated abundance estimates of 1,701 (95% confidence interval [CI]= 1,533–1,888), 1,927 (95% CI = 1,851–2,006), 2,150 (95% CI = 1,906–2,425), and 2,239 (95% CI = 1,985–2,524) individuals for seasons 1–4, respectively. This makes the population among the largest assessed of the species. Overall apparent survival was estimated as 0.958 (95% CI = 0.802–0.992). Interseasonal probabilities of transitioning to an unobservable state were estimated as 0.045, 0.363, and 0.300 for years 1–2, 2–3, and 3–4, respectively, and the overall probability of remaining in an unobservable state was 0.688. These probabilities, together with an apparent increase in abundance during the study period, indicate that the identified dolphins are part of a larger superpopulation moving throughout a more extensive geographic area. Of the photo‐identified dolphins, 28.2% exhibited injuries related to entanglements with fishing gear. This implies a strong potential for fatal interactions that could jeopardize the conservation status of the population, which otherwise appears favorable.     

Decline in abundance and apparent survival rates of fin whales (Balaenoptera physalus) in the northern Gulf of St. Lawrence

Estimates of abundance and survivorship provide quantifiable measures to monitor populations and to define and understand their conservation status. This study investigated changes in abundance and survival rates of fin whales (Balaenoptera physalus) in the northern Gulf of St. Lawrence in the context of anthropogenic pressures and changing environmental conditions. A long‐term data set, consisting of 35 years of photo‐identification surveys and comprising more than 5,000 identifications of 507 individuals, formed the basis of this mark–recapture study. Based on model selection using corrected Akaike Information Criterion, the most parsimonious Cormack–Jolly–Seber model included a linear temporal trend in noncalf apparent survival rates with a sharp decline in the last 5 years of the study and a median survival rate of 0.946 (95% confidence interval (CI) 0.910–0.967). To account for capture heterogeneity due to divergent patterns of site fidelity, agglomerative hierarchical cluster analysis was employed to categorize individuals based on their annual and survey site fidelity indices. However, the negative trend in survivorship remained and was corroborated by a significant decline in the estimated super‐population size from 335 (95% CI 321–348) individuals in 2004–2010 to 291 (95% CI 270–312) individuals in 2010–2016. Concurrently, a negative trend was estimated in recruitment to the population, supported by a sharp decrease in the number of observed calves. Ship strikes and changes in prey availability are potential drivers of the observed decline in fin whale abundance. The combination of clustering methods with mark–recapture represents a flexible way to investigate the effects of site fidelity on demographic variables and is broadly applicable to other individual‐based studies.     

Use of the Robust Design to Estimate Seasonal Abundance and Demographic Parameters of a Coastal Bottlenose Dolphin (Tursiops aduncus) Population

As delphinid populations become increasingly exposed to human activities we rely on our capacity to produce accurate abundance estimates upon which to base management decisions. This study applied mark–recapture methods following the Robust Design to estimate abundance, demographic parameters, and temporary emigration rates of an Indo-Pacific bottlenose dolphin (Tursiops aduncus) population off Bunbury, Western Australia. Boat-based photo-identification surveys were conducted year-round over three consecutive years along pre-determined transect lines to create a consistent sampling effort throughout the study period and area. The best fitting capture–recapture model showed a population with a seasonal Markovian temporary emigration with time varying survival and capture probabilities. Abundance estimates were seasonally dependent with consistently lower numbers obtained during winter and higher during summer and autumn across the three-year study period. Specifically, abundance estimates for all adults and juveniles (combined) varied from a low of 63 (95% CI 59 to 73) in winter of 2007 to a high of 139 (95% CI 134 to148) in autumn of 2009. Temporary emigration rates (γ'') for animals absent in the previous period ranged from 0.34 to 0.97 (mean  =  0.54; ±SE 0.11) with a peak during spring. Temporary emigration rates for animals present during the previous period (γ'''') were lower, ranging from 0.00 to 0.29, with a mean of 0.16 (± SE 0.04). This model yielded a mean apparent survival estimate for juveniles and adults (combined) of 0.95 (± SE 0.02) and a capture probability from 0.07 to 0.51 with a mean of 0.30 (± SE 0.04). This study demonstrates the importance of incorporating temporary emigration to accurately estimate abundance of coastal delphinids. Temporary emigration rates were high in this study, despite the large area surveyed, indicating the challenges of sampling highly mobile animals which range over large spatial areas.