Estimation of population size and trends for highly mobile species with dynamic spatial distributions

Accounting for unobserved population dynamics and aging error in close-kin mark-recapture assessments

Obtaining robust estimates of population abundance is a central challenge hindering the conservation and management of many threatened and exploited species. Close-kin mark-recapture (CKMR) is a genetics-based approach that has strong potential to improve the monitoring of data-limited species by enabling estimates of abundance, survival, and other parameters for populations that are challenging to assess. However, CKMR models have received limited sensitivity testing under realistic population dynamics and sampling scenarios, impeding the application of the method in population monitoring programs and stock assessments. Here, we use individual-based simulation to examine how unmodeled population dynamics and aging uncertainty affect the accuracy and precision of CKMR parameter estimates under different sampling strategies. We then present adapted models that correct the biases that arise from model misspecification. Our results demonstrate that a simple base-case CKMR model produces robust estimates of population abundance with stable populations that breed annually; however, if a population trend or non-annual breeding dynamics are present, or if year-specific estimates of abundance are desired, a more complex CKMR model must be constructed. In addition, we show that CKMR can generate reliable abundance estimates for adults from a variety of sampling strategies, including juvenile-focused sampling where adults are never directly observed (and aging error is minimal). Finally, we apply a CKMR model that has been adapted for population growth and intermittent breeding to two decades of genetic data from juvenile lemon sharks (Negaprion brevirostris) in Bimini, Bahamas, to demonstrate how application of CKMR to samples drawn solely from juveniles can contribute to monitoring efforts for highly mobile populations. Overall, this study expands our understanding of the biological factors and sampling decisions that cause bias in CKMR models, identifies key areas for future inquiry, and provides recommendations that can aid biologists in planning and implementing an effective CKMR study, particularly for long-lived data-limited species.

What makes Sanriku waters the southernmost habitat of northern fur seals? Winter-spring habitat use in relation to oceanographic environments

The waters off Sanriku (located on the northeastern coast of Honshu Island, Japan) provide the southernmost habitats of northern fur seals (Callorhinus ursinus) during winter and spring in the western North Pacific. The southward flowing cold Oyashio current and northward-flowing warm Kuroshio extension mix there, making the area highly productive. Northern fur seals migrate into these waters from the breeding rookeries for feeding, and the locations of the southern margins of their habitats vary yearly. The key questions for understanding the seasonal migration patterns are "why" and "how" the species utilize these waters as the southernmost habitat. We estimated the density and abundance of northern fur seals using standard line-transect theory combined with habitat modeling. The spatial patterns of animal density were analyzed using generalized additive models with seven static and dynamic environmental covariates, and those covariates were selected based on Akaike's information criterion (AIC). The lowest AIC model included depth, sea surface temperature, slope, and gradient in sea surface temperature. This model estimated well the spatial patterns of the density of the species, in which fur seals were widely distributed in the study areas, but less frequently encountered between the isobaths 100 m and 200 m. These spatially separated habitats suggest that the shelf break and offshore front play an important role in creating the feeding grounds of fur seals. On the other hand, sea surface temperature positively correlated with fur seals' density up to 14°C. This may indicate that further warm waters work as a temperature barrier, and fur seals concentrate on the edge of suitable temperature ranges.

Acoustic and visual cetacean surveys reveal year-round spatial and temporal distributions for multiple species in northern British Columbia, Canada

Cetaceans spend most of their time below the surface of the sea, highlighting the importance of passive acoustic monitoring as a tool to facilitate understanding and mapping their year-round spatial and temporal distributions. To increase our limited knowledge of cetacean acoustic detection patterns for the east and west coasts of Gwaii Haanas, a remote protected area on Haida Gwaii, BC, Canada, acoustic datasets recorded off SG̱ang Gwaay (Sep 2009-May 2011), Gowgaia Slope (Jul 2017-Jul 2019), and Ramsay Island (Aug 2018-Aug 2019) were analyzed. Comparing overlapping periods of visual surveys and acoustic monitoring confirmed presence of 12 cetacean species/species groups within the study region. Seasonal patterns were identified for blue, fin, humpback, grey and sperm whale acoustic signals. Killer whale and delphinid acoustic signals occurred year-round on both coasts of Haida Gwaii and showed strong diel variation. Cuvier's, Baird's, beaked whale and porpoise clicks, were identified in high-frequency recordings on the west coast. Correlations between environmental factors, chlorophyll-a and sea surface temperature, and cetacean acoustic occurrence off Gwaii Haanas were also examined. This study is the first to acoustically monitor Gwaii Haanas waters for an extended continuous period and therefore serves as a baseline from which to monitor future changes.

Shifting trends: Detecting changes in cetacean population dynamics in shifting habitat

The ability to monitor population dynamics and detect major changes in population trend is essential for wildlife conservation and management. However, this is often challenging for cetaceans as surveys typically cover only a portion of a population’s range and conventional stock assessment methods cannot then distinguish whether apparent changes in abundance reflect real changes in population size or shifts in distribution. We developed and tested methods for estimating population size and trend and detecting changes in population trend in the context of shifting habitat by integrating additional data into distance-sampling analysis. Previous research has shown that incorporating habitat information can improve population size estimates for highly mobile species with dynamic spatial distributions. Here, using simulated datasets representative of a large whale population, we demonstrate that incorporating individual mark-recapture data can increase the accuracy and precision of trend estimation and the power to distinguish whether apparent changes in abundance reflect changes in population trend or distribution shifts. We recommend that similar simulation studies are conducted for specific cetacean populations to assess the potential for detecting changes in population dynamics given available data. This approach is especially important wherever population change may be confounded with long-term change in distribution patterns associated with regime shifts or climate change.

Aerial survey estimates of polar bears and their tracks in the Chukchi Sea

Polar bears are of international conservation concern due to climate change but are difficult to study because of low densities and an expansive, circumpolar distribution. In a collaborative U.S.-Russian effort in spring of 2016, we used aerial surveys to detect and estimate the abundance of polar bears on sea ice in the Chukchi Sea. Our surveys used a combination of thermal imagery, digital photography, and human observations. Using spatio-temporal statistical models that related bear and track densities to physiographic and biological covariates (e.g., sea ice extent, resource selection functions derived from satellite tags), we predicted abundance and spatial distribution throughout our study area. Estimates of 2016 abundance ([Formula: see text]) ranged from 3,435 (95% CI: 2,300-5,131) to 5,444 (95% CI: 3,636-8,152) depending on the proportion of bears assumed to be missed on the transect line during Russian surveys (g(0)). Our point estimates are larger than, but of similar magnitude to, a recent estimate for the period 2008-2016 ([Formula: see text]; 95% CI 1,522-5,944) derived from an integrated population model applied to a slightly smaller area. Although a number of factors (e.g., equipment issues, differing platforms, low sample sizes, size of the study area relative to sampling effort) required us to make a number of assumptions to generate estimates, it establishes a useful lower bound for abundance, and suggests high spring polar bear densities on sea ice in Russian waters south of Wrangell Island. With future improvements, we suggest that springtime aerial surveys may represent a plausible avenue for studying abundance and distribution of polar bears and their prey over large, remote areas.

Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence-only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991-2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest.

Detection and classification of narrow-band high frequency echolocation clicks from drifting recorders

In the California Current off the United States West Coast, there are three offshore cetacean species that produce narrow-band high frequency (NBHF) echolocation pulses: Dall's porpoise (Phocoenoides dalli) and two species of Kogia. NBHF pulses exist in a highly specialized acoustic niche thought to be outside the hearing range of killer whales and other potential mammal-eating odontocetes. Very little is known about the dwarf and pygmy sperm whales (K. sima and K. breviceps), including their NBHF pulse characteristics. This paper presents a multivariate clustering method using data from unmanned drifting acoustic recorders and visually verified porpoise recordings to discriminate between probable porpoise and Kogia clicks. Using density clustering, this study finds three distinct clusters whose geographic distributions are consistent with the known habitat range for Kogia and Dall's porpoise. A Random Forest classification model correctly assigned 97% of the clicks to their cluster. Visually verified Dall's porpoise clicks from towed hydrophones were strongly associated with one of the clusters, while a second cluster tended to be outside the geographic range of Dall's porpoise and unlike the Dall's porpoise cluster. These clicks, presumed to be made by Kogia, exhibited greater spectral variance than previous Kogia echolocation studies. It is possible that the structure of Kogia NBHF pulses may not be as stereotypical as previously described.

Bayesian Model Selection in Fisheries Management and Ecology

Researchers often test ecological hypotheses relating to a myriad of questions ranging from assemblage structure, population dynamics, demography, abundance, growth rate, and more using mathematical models that explain trends in data. To aid in the evaluation process when faced with competing hypotheses, we employ statistical methods to evaluate the validity of these multiple hypotheses with the goal of deriving the most robust conclusions possible. In fisheries management and ecology, frequentist methodologies have largely dominated this approach. However, in recent years, researchers have increasingly used Bayesian inference methods to estimate model parameters. Our aim with this perspective is to provide the practicing fisheries ecologist with an accessible introduction to Bayesian model selection. Here we discuss Bayesian inference methods for model selection in the context of fisheries management and ecology with empirical examples to guide researchers in the use of these methods. In this perspective we discuss three methods for selecting among competing models. For comparing two models we discuss Bayes factor and for more complex models we discuss Watanabe–Akaike information criterion and leave-one-out cross-validation. We also describe what kinds of information to report when conducting Bayesian inference. We conclude this review with a discussion of final thoughts about these model selection techniques.