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The recovery of large carnivore species from over-exploitation can have socioecological effects; thus, reliable estimates of potential abundance and distribution represent a valuable tool for developing management objectives and recovery criteria. For sea otters (Enhydra lutris), as with many apex predators, equilibrium abundance is not constant across space but rather varies as a function of local habitat quality and resource dynamics, thereby complicating the extrapolation of carrying capacity (K) from one location to another. To overcome this challenge, we developed a state-space model of density-dependent population dynamics in southern sea otters (E. l. nereis), in which K is estimated as a continuously varying function of a suite of physical, biotic, and oceanographic variables, all described at fine spatial scales. We used a theta-logistic process model that included environmental stochasticity and allowed for density-independent mortality associated with shark bites. We used Bayesian methods to fit the model to time series of survey data, augmented by auxiliary data on cause of death in stranded otters. Our model results showed that the expected density at K for a given area can be predicted based on local bathymetry (depth and distance from shore), benthic substrate composition (rocky vs. soft sediments), presence of kelp canopy, net primary productivity, and whether or not the area is inside an estuary. In addition to density-dependent reductions in growth, increased levels of shark-bite mortality over the last decade have also acted to limit population expansion. We used the functional relationships between habitat variables and equilibrium density to project estimated values of K for the entire historical range of southern sea otters in California, USA, accounting for spatial variation in habitat quality. Our results suggest that California could eventually support 17,226 otters (95% CrI=9,739–30,087). We also used the fitted model to compute candidate values of optimal sustainable population abundance (OSP) for all of California and for regions within California. We employed a simulation-based approach to determine the abundance associated with the maximum net productivity level (MNPL) and propose that the upper quartile of the distribution of MNPL estimates (accounting for parameter uncertainty) represents an appropriate threshold value for OSP. Based on this analysis, we suggest a candidate value for OSP (for all of California) of 10,236, which represents 59.4% of projected K.

To successfully respond to changing habitat, climate or harvest, managers need to identify the most effective strategies to reverse population trends of declining species and/or manage harvest of game species. A classic approach in conservation biology for the last two decades has been the use of matrix population models to determine the most important vital rates affecting population growth rate (λ), that is, sensitivity. Ecologists quickly realized the critical role of environmental variability in vital rates affecting λ by developing approaches such as life-stage simulation analysis (LSA) that account for both sensitivity and variability of a vital rate. These LSA methods used matrix-population modeling and Monte Carlo simulation methods, but faced challenges in integrating data from different sources, disentangling process and sampling variation, and in their flexibility. Here, we developed a Bayesian integrated population model (IPM) for two populations of a large herbivore, elk (Cervus canadensis) in Montana, USA. We then extended the IPM to evaluate sensitivity in a Bayesian framework. We integrated known-fate survival data from radio-marked adults and juveniles, fecundity data, and population counts in a hierarchical population model that explicitly accounted for process and sampling variance. Next, we tested the prevailing paradigm in large herbivore population ecology that juvenile survival of neonates <90 d old drives λ using our Bayesian LSA approach. In contrast to the prevailing paradigm in large herbivore ecology, we found that adult female survival explained more of the variation in λ than elk calf survival, and that summer and winter elk calf survival periods were nearly equivalent in importance for λ. Our Bayesian IPM improved precision of our vital rate estimates and highlighted discrepancies between count and vital rate data that could refine population monitoring, demonstrating that combining sensitivity analysis with population modeling in a Bayesian framework can provide multiple advantages. Our Bayesian LSA framework will provide a useful approach to addressing conservation challenges across a variety of species and data types.

Understanding changes in abundance is crucial for conservation, but population growth rates often vary over space and time. We use 40 years of count data (1979–2019) and Bayesian state-space models to assess the African penguin Spheniscus demersus population under IUCN Red List Criterion A. We deconstruct the overall decline in time and space to identify where urgent conservation action is needed. The global African penguin population met the threshold for Endangered with a high probability (97%), having declined by almost 65% since 1989. An historical low of ~17,700 pairs bred in 2019. Annual changes were faster in the South African population (−4.2%, highest posterior density interval, HPDI: −7.8 to −0.6%) than the Namibian one (−0.3%, HPDI: −3.3 to +2.6%), and since 1999 were almost −10% at South African colonies north of Cape Town. Over the 40-year period, the Eastern Cape colonies went from holding ~25% of the total penguin population to ~40% as numbers decreased more rapidly elsewhere. These changes coincided with an altered abundance and availability of the main prey of African penguins. Our results underline the dynamic nature of population declines in space as well as time and highlight which penguin colonies require urgent conservation attention.

Harvesting large carnivores can be a management tool for meeting politically set goals for their desired abundance. However, harvesting carnivores creates its own set of conflicts in both society and among conservation professionals, where one consequence is a need to demonstrate that management is sustainable, evidence‐based and guided by science. Furthermore, because large carnivores often also have high degrees of legal protection, harvest quotas have to be carefully justified and constantly adjusted to avoid damaging their conservation status. We developed a Bayesian state‐space model to support adaptive management of Eurasian lynx harvesting in Scandinavia. The model uses data from the annual monitoring of lynx abundance and results from long‐term field research on lynx biology, which has provided detailed estimates of key demographic parameters. We used the model to predict the probability that the forecasted population size will be below or above the management objectives when subjected to different harvest quotas. The model presented here informs decision makers about the policy risks of alternative harvest levels. Earlier versions of the model have been available for wildlife managers in both Sweden and Norway to guide lynx harvest quotas and the model predictions showed good agreement with observations. We combined monitoring data with data on vital rates and were able to estimate unobserved additional mortality rates, which are most probably due to poaching. In both countries, the past quota setting strategy suggests that there has been a de facto threshold strategy with increasing proportion, which means that there is no harvest below a certain population size, but above this threshold there is an increasing proportion of the population harvested as the population size increases. The annual assessment of the monitoring results, the use of forecasting models, and a threshold harvest approach to quota setting will all reduce the risk of lynx population sizes moving outside the desired goals. The approach we illustrate could be adapted to other populations of mammals worldwide.

Despite the purported role of avian pox (Avipoxvirus spp.) in the decline of endemic Hawaiian birds, few studies have been conducted on the dynamics of this disease, its impact on freeliving avian populations, or its interactions with avian malaria (Plasmodium relictum). We conducted four longitudinal studies of 3–7 yr in length and used generalized linear models to evaluate cross-sectional prevalence of active pox infection and individuals with healed deformities that had recovered from pox. Our goal was to understand how species, season, elevation, malaria infection, and other biological characteristics influenced pox infection in ʻApapane, Hawaiʻi ʻAmakihi, ʻIʻiwi, and Japanese White-eye across low-, mid-, and high-elevation forests on the island of Hawaiʻi. We also used multistate capture-recapture (longitudinal) models to estimate pox infection rates, recovery rates, and potential pox-related mortality. Pox infection rates were typically highest in low-elevation forests, followed by mid-elevation forests, and lowest in high-elevation forests. We also found seasonal changes in pox prevalence throughout the annual cycle; typically increasing from spring through summer, peaking in fall, and declining in winter. These seasonal changes occurred in low- and mid-elevation forests, but not in high elevations where pox infection was low. Seasonal and elevation patterns of pox infection are like those for avian malaria, strongly implicating mosquito vectors, rather than other biting arthropods or contact transmission, as the primary source of transmitting both diseases. Most native Hawaiian birds recovered from pox infection within 6 months; frequently without permanent lesions. Contrary to our expectations, we found no direct evidence that pox is a substantial mortality factor in any of the three native bird species we studied. Birds with chronic malaria infection were more likely to have both active pox infection and healed pox lesions suggesting a synergistic interaction that may influence the evolution of pox virulence. Because pox infection can be assessed visually, and birds have a high recovery rate, this disease may be a sensitive indicator of the seasonal and annual risk of transmission of malaria in Hawaiʻi.

Understanding how natural and anthropogenic processes affect population dynamics of species with patchy distributions is critical to predicting their responses to environmental changes. Despite considerable evidence that demographic rates and dispersal patterns vary temporally in response to an array of biotic and abiotic processes, few applications of metapopulation theory have sought to explore factors that explain spatiotemporal variation in extinction or colonization rates. To facilitate exploring these factors, we extended a spatially explicit model of metapopulation dynamics to create a framework that requires only binary presence–absence data, makes few assumptions about the dispersal process, and accounts for imperfect detection. We apply this framework to 22 yr of biannual survey data for lowland leopard frogs, Lithobates yavapaiensis, an amphibian that inhabits arid stream systems in the southwestern United States and northern Mexico. Our results highlight the importance of accounting for factors that govern temporal variation in transition probabilities, as both extinction and colonization rates varied with hydrologic conditions. Specifically, local extinctions were more frequent during drought periods, particularly at sites without reliable surface water. Colonization rates increased when larval and dispersal periods were wetter than normal, which increased the probability that potential emigrants metamorphosed and reached neighboring sites. Extirpation of frogs from all sites in one watershed during a period of severe drought demonstrated the influence of site-level features, as frogs persisted only in areas where most sites held water consistently and where the amount of sediment deposited from high-elevation wildfires was low. Application of our model provided novel insights into how climate-related processes affected the distribution and population dynamics of an arid-land amphibian. The approach we describe has application to a wide array of species that inhabit patchy environments, can improve our understanding of factors that govern metapopulation dynamics, and can inform strategies for conservation of imperiled species.

1. An understanding of the underlying processes and comprehensive history of invasive species is necessary to assess the long-term effectiveness of invasive species management. However, continuous, long-term labour-intensive population surveys on invasive species are often not feasible. Thus, it is important to learn about their dynamics through management action and its consequences. 2. Amami Island, Japan, has an ongoing large-scale and long-term eradication programme of invasive small Indian mongooses. To estimate the long-term pattern of population size and the parameters determining the dynamics, including anthropogenic removal, we applied a surplus-production model within a Bayesian state-space formulation incorporating the initial population size, number of captures and capture effort. Using the estimated process model directly, we conducted stochastic simulations to evaluate the feasibility of eradication. 3. Estimated 32-year annual capture probability of mongooses has increased since their introduction. The population size started to decline in 2001; mean population size in 2000 was 6141 (95% CI: 5415–6817), and declined to 169 (95% CI: 42–408) by 2011. Parameter estimates of a Weibull catchability model indicated that there was large individual heterogeneity in the probability of being captured, and per-effort capture probability declined with an increase in annual capture effort. 4. The simulation study indicated that the eradication feasibility in 2023 would be over 90% if the same annual capture effort is upheld as in 2010 (2 075 760 corrected trap-days). However, increasing annual capture effort would have little effect on shortening the time to eradication. 5. Synthesis and applications. A hierarchical model that incorporates multiple types of data to reveal long-term population dynamics has the potential to be updated with the outcomes of control efforts, and will enhance adaptive management of invasive species. This approach will offer valuable information about trade-offs between time to eradication success and effort per unit time in a long-term eradication project, and the length of time needed to continue management actions to achieve eradication success.

1. To successfully manipulate populations for management and conservation purposes, managers must be able to track changes in demographic rates and determine the factors driving spatial and temporal variation in those rates. For populations of management concern, however, data deficiencies frequently limit the use of traditional statistical methods for such analyses. Long-term demographic data are often piecemeal, having small sample sizes, inconsistent methodologies, intermittent data, and information on only a subset of important parameters and covariates. 2. We evaluated the effectiveness of Bayesian state-space models for meeting these data limitations in elucidating dynamics of federally endangered Sierra Nevada bighorn sheep Ovis canadensis sierrae. We combined ground count, telemetry, and mark-resight data to: (1) estimate demographic parameters in three populations (including stage-specific abundances and vital rates); and (2) determine whether density, summer precipitation, or winter severity were driving variation in key demographic rates. 3. Models combining all existing data types increased the precision and accuracy in parameter estimates and fit covariates to vital rates driving population performance. They also provided estimates for all years of interest (including years in which field data were not collected) and standardized the error structure across data types. 4. Demographic rates indicated that recovery efforts should focus on increasing adult and yearling survival in the smallest bighorn sheep population. In evaluating covariates we found evidence of negative density dependence in the larger herds, but a trend of positive density dependence in the smallest herd suggesting that an augmentation may be needed to boost performance. We also found that vital rates in all populations were positively associated with summer precipitation, but that winter severity only had a negative effect on the smallest herd, the herd most strongly impacted by environmental stochasticity. 5. Synthesis and applications. For populations with piecemeal data, a problem common to both endangered and harvested species, obtaining precise demographic parameter estimates is one of the greatest challenges in detecting population trends, diagnosing the causes of decline, and directing management. Data on Sierra Nevada bighorn sheep provide an example of the application of Bayesian state-space models for combining all existing data to meet these objectives and better inform important management and conservation decisions.

Density dependence, population regulation, and variability in population size are fundamental population processes, the manifestation and interrelationships of which are affected by environmental variability. However, there are surprisingly few empirical studies that distinguish the effect of environmental variability from the effects of population processes. We took advantage of a unique system, in which populations of the same duck species or close ecological counterparts live in highly variable (north American prairies) and in stable (north European lakes) environments, to distinguish the relative contributions of environmental variability (measured as between‐year fluctuations in wetland numbers) and intraspecific interactions (density dependence) in driving population dynamics. We tested whether populations living in stable environments (in northern Europe) were more strongly governed by density dependence than populations living in variable environments (in North America). We also addressed whether relative population dynamical responses to environmental variability versus density corresponded to differences in life history strategies between dabbling (relatively “fast species” and governed by environmental variability) and diving (relatively “slow species” and governed by density) ducks. As expected, the variance component of population fluctuations caused by changes in breeding environments was greater in North America than in Europe. Contrary to expectations, however, populations in more stable environments were not less variable nor clearly more strongly density dependent than populations in highly variable environments. Also, contrary to expectations, populations of diving ducks were neither more stable nor stronger density dependent than populations of dabbling ducks, and the effect of environmental variability on population dynamics was greater in diving than in dabbling ducks. In general, irrespective of continent and species life history, environmental variability contributed more to variation in species abundances than did density. Our findings underscore the need for more studies on populations of the same species in different environments to verify the generality of current explanations about population dynamics and its association with species life history. We took advantage of a unique system, in which populations of the same duck species or close ecological counterparts live in highly variable (North American prairies) and in stable (north European lakes) environments, to distinguish the relative contributions of environmental variability and intraspecific interactions (density dependence) in driving population dynamics. We also addressed possible associations between species life history strategy and population dynamical responses to environmental variability by examining differences between dabbling ducks (relatively “fast species”) and diving ducks (relatively “slow species”) in population variability and in the relative importance of density dependence as a driver of population dynamics. We found that, in general, irrespective of continent and species life history, environmental variability contributed more to the variation in species abundances than did density dependence.