Robustly estimating the demographic contribution of immigration: Simulation, sensitivity analysis and seals

Identifying important demographic drivers of population dynamics is fundamental for understanding life-history evolution and implementing effective conservation measures. Integrated population models (IPMs) coupled with transient life table response experiments (tLTREs) allow ecologists to quantify the contributions of demographic parameters to observed population change. While IPMs can estimate parameters that are not estimable using any data source alone, for example, immigration, the estimated contribution of such parameters to population change is prone to bias. Currently, it is unclear when robust conclusions can be drawn from them. We sought to understand the drivers of a rebounding southern elephant seal population on Marion Island using the IPM-tLTRE framework, applied to count and mark-recapture data on 9500 female seals over nearly 40 years. Given the uncertainty around IPM-tLTRE estimates of immigration, we also aimed to investigate the utility of simulation and sensitivity analyses as general tools for evaluating the robustness of conclusions obtained in this framework. Using a Bayesian IPM and tLTRE analysis, we quantified the contributions of survival, immigration and population structure to population growth. We assessed the sensitivity of our estimates to choice of multivariate priors on immigration and other vital rates. To do so we make a novel application of Gaussian process priors, in comparison with commonly used shrinkage priors. Using simulation, we assessed our model's ability to estimate the demographic contribution of immigration under different levels of temporal variance in immigration. The tLTRE analysis suggested that adult survival and immigration were the most important drivers of recent population growth. While the contribution of immigration was sensitive to prior choices, the estimate was consistently large. Furthermore, our simulation study validated the importance of immigration by showing that our estimate of its demographic contribution is unlikely to result as a biased overestimate. Our results highlight the connectivity between distant populations of southern elephant seals, illustrating that female dispersal can be important in regulating the abundance of local populations even when natal site fidelity is high. More generally, we demonstrate how robust ecological conclusions may be obtained about immigration from the IPM-tLTRE framework, by combining sensitivity analysis and simulation.

Ninety years of change, from commercial extinction to recovery, range expansion and decline for Antarctic fur seals at South Georgia

With environmental change, understanding how species recover from overharvesting and maintain viable populations is central to ecosystem restoration. Here, we reconstruct 90 years of recovery trajectory of the Antarctic fur seal at South Georgia (S.W. Atlantic), a key indicator species in the krill-based food webs of the Southern Ocean. After being harvested to commercial extinction by 1907, this population rebounded and now constitutes the most abundant otariid in the World. However, its status remains uncertain due to insufficient and conflicting data, and anthropogenic pressures affecting Antarctic krill, an essential staple for millions of fur seals and other predators. Using integrated population models, we estimated simultaneously the long-term abundance for Bird Island, northwest South Georgia, epicentre of recovery of the species after sealing, and population adjustments for survey counts with spatiotemporal applicability. Applied to the latest comprehensive survey data, we estimated the population at South Georgia in 2007-2009 as 3,510,283 fur seals [95% CI: 3,140,548-3,919,604] (ca. 98% of global population), after 40 years of maximum growth and range expansion owing to an abundant krill supply. At Bird Island, after 50 years of exponential growth followed by 25 years of slow stable growth, the population collapsed in 2009 and has thereafter declined by -7.2% [-5.2, -9.1] per annum, to levels of the 1970s. For the instrumental record, this trajectory correlates with a time-varying relationship between coupled climate and sea surface temperature cycles associated with low regional krill availability, although the effects of increasing krill extraction by commercial fishing and natural competitors remain uncertain. Since 2015, fur seal longevity and recruitment have dropped, sexual maturation has retarded, and population growth is expected to remain mostly negative and highly variable. Our analysis documents the rise and fall of a key Southern Ocean predator over a century of profound environmental and ecosystem change.

Partitioning variance in population growth for models with environmental and demographic stochasticity

How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual 'realized' growth rates using 'transient' LTREs have gained in popularity. Transient LTREs have been used particularly to understand how variation in vital rates translate into variation in growth for populations under long-term study. For these, complete population models may be constructed to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking. We extend transient LTREs to further partition the contributions from vital rates into contributions from temporally varying factors that affect them. The decomposition allows one to compare the resultant effect on the growth rate of different environmental factors, as well as density dependence, which may each act via multiple vital rates. We also show how realized growth rates can be decomposed into separate components from environmental and demographic stochasticity. The latter is typically omitted in LTRE analyses. We illustrate these extensions with an integrated population model (IPM) for data from a 26 years study on northern wheatears (Oenanthe oenanthe), a migratory passerine bird breeding in an agricultural landscape. For this population, consisting of around 50-120 breeding pairs per year, we partition variation in realized growth rates into environmental contributions from temperature, rainfall, population density and unexplained random variation via multiple vital rates, and from demographic stochasticity. The case study suggests that variation in first year survival via the unexplained random component, and adult survival via temperature are two main factors behind environmental variation in growth rates. More than half of the variation in growth rates is suggested to come from demographic stochasticity, demonstrating the importance of this factor for populations of moderate size.

Consequences of cross-season demographic correlations for population viability

Demographic correlations are pervasive in wildlife populations and can represent important secondary drivers of population growth. Empirical evidence suggests that correlations are in general positive for long-lived species, but little is known about the degree of variation among spatially segregated populations of the same species in relation to environmental conditions. We assessed the relative importance of two cross-season correlations in survival and productivity, for three Atlantic puffin (Fratercula arctica) populations with contrasting population trajectories and non-overlapping year-round distributions. The two correlations reflected either a relationship between adult survival prior to breeding on productivity, or a relationship between productivity and adult survival the subsequent year. Demographic rates and their correlations were estimated with an integrated population model, and their respective contributions to variation in population growth were calculated using a transient-life table response experiment. For all three populations, demographic correlations were positive at both time lags, although their strength differed. Given the different year-round distributions of these populations, this variation in the strength population-level demographic correlations points to environmental conditions as an important driver of demographic variation through life-history constraints. Consequently, the contributions of variances and correlations in demographic rates to population growth rates differed among puffin populations, which has implications for-particularly small-populations' viability under environmental change as positive correlations tend to reduce the stochastic population growth rate.

Immigration as the main driver of population dynamics in a cryptic cetacean

Empirical evidence about the role and interaction of immigration with local demographic processes in shaping population dynamics is still scarce. This knowledge gap limits our capability to derive a conceptual framework that can be used to inform conservation actions. Populations exposed to nonstationary environment do not converge to a stable stage distribution, implying the need for evaluating the demographic role of both vital rates and stage distribution using appropriate tools. This is particularly important for species with larger generation times like cetaceans. We explored the relative demographic role of vital rates and population structure of a poorly known cetacean, the Mediterranean Cuvier's beaked whale, while accounting for the exposure to nonstationary environments. We performed a retrospective analysis through transient life table response experiments (tLTRE) using demographic rates and population structure of both sexes obtained from an integrated population model. The contribution of immigration to variation in realized population growth rates was 4.2, 7.6, and 12.7 times larger than that of female apparent survival, proportional abundance of breeding females with a 2-year-old calf, and proportional abundance of breeding females with a 3-year-old calf, respectively. Immigration rate and proportional abundance of breeding females with a 2- or 3-year-old calf explained, respectively, 65% and 20% of total temporal variability in realized population growth rates. Changes in realized population growth rate between successive years were mainly driven by changes in immigration and population structure, specifically the proportional abundance of breeding females with a 2-year-old calf. Our study provides insight into the demographic processes that affect population dynamics and in a cryptic cetacean. We presented an analytical approach for maximizing the use of available data through the integration of multiple sources of information for individuals of different distinctiveness levels.

Spatial consistency in drivers of population dynamics of a declining migratory bird

Many migratory species are in decline across their geographical ranges. Single-population studies can provide important insights into drivers at a local scale, but effective conservation requires multi-population perspectives. This is challenging because relevant data are often hard to consolidate, and state-of-the-art analytical tools are typically tailored to specific datasets. We capitalized on a recent data harmonization initiative (SPI-Birds) and linked it to a generalized modelling framework to identify the demographic and environmental drivers of large-scale population decline in migratory pied flycatchers (Ficedula hypoleuca) breeding across Britain. We implemented a generalized integrated population model (IPM) to estimate age-specific vital rates, including their dependency on environmental conditions, and total and breeding population size of pied flycatchers using long-term (34-64 years) monitoring data from seven locations representative of the British breeding range. We then quantified the relative contributions of different vital rates and population structure to changes in short- and long-term population growth rate using transient life table response experiments (LTREs). Substantial covariation in population sizes across breeding locations suggested that change was the result of large-scale drivers. This was supported by LTRE analyses, which attributed past changes in short-term population growth rates and long-term population trends primarily to variation in annual survival and dispersal dynamics, which largely act during migration and/or nonbreeding season. Contributions of variation in local reproductive parameters were small in comparison, despite sensitivity to local temperature and rainfall within the breeding period. We show that both short- and long-term population changes of British breeding pied flycatchers are likely linked to factors acting during migration and in nonbreeding areas, where future research should be prioritized. We illustrate the potential of multi-population analyses for informing management at (inter)national scales and highlight the importance of data standardization, generalized and accessible analytical tools, and reproducible workflows to achieve them.

Density-dependence produces spurious relationships among demographic parameters in a harvested species

Harvest of wild organisms is an important component of human culture, economy, and recreation, but can also put species at risk of extinction. Decisions that guide successful management actions therefore rely on the ability of researchers to link changes in demographic processes to the anthropogenic actions or environmental changes that underlie variation in demographic parameters. Ecologists often use population models or maximum sustained yield curves to estimate the impacts of harvest on wildlife and fish populations. Applications of these models usually focus exclusively on the impact of harvest and often fail to consider adequately other potential, often collinear, mechanistic drivers of the observed relationships between harvest and demographic rates. In this study, we used an integrated population model and long-term data (1973-2016) to examine the relationships among hunting and natural mortality, the number of hunters, habitat conditions, and population size of blue-winged teal Spatula discors, an abundant North American dabbling duck with a relatively fast-paced life history strategy. Over the last two and a half decades of the study, teal abundance tripled, hunting mortality probability increased slightly ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>&lt;</mml:mo> <mml:mn>0.02</mml:mn></mml:mrow> </mml:semantics> </mml:math> ), and natural mortality probability increased substantially ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>&gt;</mml:mo> <mml:mn>0.1</mml:mn></mml:mrow> </mml:semantics> </mml:math> ) at greater population densities. We demonstrate strong density-dependent effects on natural mortality and fecundity as population density increased, indicative of compensatory harvest mortality and compensatory natality. Critically, an analysis that only assessed the relationship between survival and hunting mortality would spuriously indicate depensatory mortality due to multicollinearity between abundance, natural mortality and hunting mortality. Our findings demonstrate that models that only consider the direct effect of hunting on survival or natural mortality can fail to accurately assess the mechanistic impact of hunting on population dynamics due to multicollinearity among demographic drivers. This multicollinearity limits inference and may have strong impacts on applied management actions globally.

Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance

Abundance estimation is a critical component of conservation planning, particularly for exploited species where managers set regulations to restrict harvest based on current population size. An increasingly common approach for abundance estimation is through integrated population modeling (IPM), which uses multiple data sources in a joint likelihood to estimate abundance and additional demographic parameters. Lincoln estimators are one commonly used IPM component for harvested species, which combine information on the rate and total number of individuals harvested within an integrated band‐recovery framework to estimate abundance at large scales. A major assumption of the Lincoln estimator is that banding and recoveries are representative of the whole population, which may be violated if major sources of spatial heterogeneity in survival or harvest rates are not incorporated into the model. We developed an approach to account for spatial variation in harvest rates using a spatial predictive process, which we incorporated into a Lincoln estimator IPM. We simulated data under different configurations of sample sizes, harvest rates, and sources of spatial heterogeneity in harvest rate to assess potential model bias in parameter estimates. We then applied the model to data collected from a field study of wild turkeys (Meleagris gallapavo) to estimate local and statewide abundance in Maine, USA. We found that the band recovery model that incorporated a spatial predictive process consistently provided estimates of adult and juvenile abundance with low bias across a variety of spatial configurations of harvest rate and sampling intensities. When applied to data collected on wild turkeys, a model that did not incorporate spatial heterogeneity underestimated the harvest rate in some subregions. Consistent with simulation results, this led to overestimation of both local and statewide abundance. Our work demonstrates that a spatial predictive process is a viable mechanism to account for spatial variation in harvest rates and limit bias in abundance estimates. This approach could be extended to large‐scale band recovery data sets and has applicability for the estimation of population parameters in other ecological models as well.

An integrated path for spatial capture-recapture and animal movement modeling

Ecologists and conservation biologists increasingly rely on spatial capture-recapture (SCR) and movement modeling to study animal populations. Historically, SCR has focused on population-level processes (e.g., vital rates, abundance, density, and distribution), whereas animal movement modeling has focused on the behavior of individuals (e.g., activity budgets, resource selection, migration). Even though animal movement is clearly a driver of population-level patterns and dynamics, technical and conceptual developments to date have not forged a firm link between the two fields. Instead, movement modeling has typically focused on the individual level without providing a coherent scaling from individual- to population-level processes, whereas SCR has typically focused on the population level while greatly simplifying the movement processes that give rise to the observations underlying these models. In our view, the integration of SCR and animal movement modeling has tremendous potential for allowing ecologists to scale up from individuals to populations and advancing the types of inferences that can be made at the intersection of population, movement, and landscape ecology. Properly accounting for complex animal movement processes can also potentially reduce bias in estimators of population-level parameters, thereby improving inferences that are critical for species conservation and management. This introductory article to the Special Feature reviews recent advances in SCR and animal movement modeling, establishes a common notation, highlights potential advantages of linking individual-level (Lagrangian) movements to population-level (Eulerian) processes, and outlines a general conceptual framework for the integration of movement and SCR models. We then identify important avenues for future research, including key challenges and potential pitfalls in the developments and applications that lie ahead.

Demographic responses to climate-driven variation in habitat quality across the annual cycle of a migratory bird species

The demography and dynamics of migratory bird populations depend on patterns of movement and habitat quality across the annual cycle. We leveraged archival GPS‐tagging data, climate data, remote‐sensed vegetation data, and bird‐banding data to better understand the dynamics of black‐headed grosbeak (Pheucticus melanocephalus) populations in two breeding regions, the coast and Central Valley of California (Coastal California) and the Sierra Nevada mountain range (Sierra Nevada), over 28 years (1992–2019). Drought conditions across the annual cycle and rainfall timing on the molting grounds influenced seasonal habitat characteristics, including vegetation greenness and phenology (maturity dates). We developed a novel integrated population model with population state informed by adult capture data, recruitment rates informed by age‐specific capture data and climate covariates, and survival rates informed by adult capture–mark–recapture data and climate covariates. Population size was relatively variable among years for Coastal California, where numbers of recruits and survivors were positively correlated, and years of population increase were largely driven by recruitment. In the Sierra Nevada, population size was more consistent and showed stronger evidence of population regulation (numbers of recruits and survivors negatively correlated). Neither region showed evidence of long‐term population trend. We found only weak support for most climate–demographic rate relationships. However, recruitment rates for the Coastal California region were higher when rainfall was relatively early on the molting grounds and when wintering grounds were relatively cool and wet. We suggest that our approach of integrating movement, climate, and demographic data within a novel modeling framework can provide a useful method for better understanding the dynamics of broadly distributed migratory species.

Age-specific survival rates, causes of death, and allowable take of golden eagles in the western United States

In the United States, the Bald and Golden Eagle Protection Act prohibits take of golden eagles (Aquila chrysaetos) unless authorized by permit, and stipulates that all permitted take must be sustainable. Golden eagles are unintentionally killed in conjunction with many lawful activities (e.g., electrocution on power poles, collision with wind turbines). Managers who issue permits for incidental take of golden eagles must determine allowable take levels and manage permitted take accordingly. To aid managers in making these decisions in the western United States, we used an integrated population model to obtain estimates of golden eagle vital rates and population size, and then used those estimates in a prescribed take level (PTL) model to estimate the allowable take level. Estimated mean annual survival rates for golden eagles ranged from 0.70 (95% credible interval = 0.66-0.74) for first-year birds to 0.90 (0.88-0.91) for adults. Models suggested a high proportion of adult female golden eagles attempted to breed and breeding pairs fledged a mean of 0.53 (0.39-0.72) young annually. Population size in the coterminous western United States has averaged ~31,800 individuals for several decades, with λ = 1.0 (0.96-1.05). The PTL model estimated a median allowable take limit of ~2227 (708-4182) individuals annually given a management objective of maintaining a stable population. We estimate that take averaged 2572 out of 4373 (59%) deaths annually, based on a representative sample of transmitter-tagged golden eagles. For the subset of golden eagles that were recovered and a cause of death determined, anthropogenic mortality accounted for an average of 74% of deaths after their first year; leading forms of take over all age classes were shooting (~670 per year), collisions (~611), electrocutions (~506), and poisoning (~427). Although observed take overlapped the credible interval of our allowable take estimate and the population overall has been stable, our findings indicate that additional take, unless mitigated for, may not be sustainable. Our analysis demonstrates the utility of the joint application of integrated population and prescribed take level models to management of incidental take of a protected species.

Can attraction to and competition for high-quality habitats shape breeding propensity?

In many animal species, sexually mature individuals may skip breeding opportunities despite a likely negative impact on fitness. In spatio‐temporally heterogeneous environments, habitat selection theory predicts that individuals select habitats where fitness prospects are maximized. Individuals are attracted to high‐quality habitat patches where they compete for high‐quality breeding sites. Since failures in contests to secure a site may prevent individuals from breeding, we hypothesized that attraction to and competition for high‐quality habitats could shape breeding propensity.

Under this hypothesis, we predicted the two following associations between breeding propensity and two key population features. (1) When mean habitat quality in the population increases in multiple patches such that availability of high‐quality sites increases across the population, the resulting decrease in competition should positively affect breeding propensity. (2) When the number of individuals increases in the population, the resulting increase in competitors should negatively affect breeding propensity (negative density dependence).

Using long‐term data from kittiwakes Rissa tridactyla, we checked the prerequisite of prediction (1), that availability of high‐quality sites is positively associated with current mean habitat quality in the population (represented by breeding success). We then applied integrated population modelling to quantify annual fluctuations in population mean breeding success, breeding propensity and number of individuals by breeding status (pre‐breeders, breeders, skippers and immigrants), and tested our predictions.

Our results showed that breeding propensity acts as an important driver of population growth. As expected, breeding propensity was positively associated with preceding mean habitat quality in the population, and negatively with the number of competitors. These relationships varied depending on breeding status, which likely reflects status dependence in competitive ability.

These findings highlight the importance of competition for high‐quality breeding sites in shaping breeding propensity. Thereby, we draw attention towards alternative and complementary explanations to more standard considerations regarding the energetic cost of reproduction, and point to possible side effects of habitat selection behaviours on individual life histories and population dynamics.

Partial and complete dependency among data sets has minimal consequence on estimates from integrated population models

Integrated population models (IPMs) are widely used to combine disparate data sets in joint analysis to better understand population dynamics and provide guidance for conservation activities. An often-cited assumption of IPMs is independence among component data sets within the combined likelihood. Dependency among data sets should lead to underestimation of variance and bias because individuals contribute data to more than one data set. In practice, studied individuals often occur in multiple data sets in IPMs (i.e., overlap), which is one way for the independence assumption to be violated. Such cases have the potential to dissuade practitioners and limit application of IPMs to solve emerging ecological problems. We assessed precision and bias of demographic rates estimated from IPMs using a complete gradient (0-100%) of overlap among data sets, wide ranges in demographic rates (e.g., survival 0.1-0.8) and sample sizes (100-1,200 individuals) and variable data sources. We compared results from our simulations with those from IPMs constructed using empirical data on tree swallows (Tachycineta bicolor) where data sets either had complete overlap or included different individuals. Contrary to previous investigators, we found no substantive bias or uncertainty in any demographic rate from IPMs derived from data sets with complete overlap. While variability in demographic rates was greater at low sample sizes (i.e., low capture, recapture, and survey probabilities), there were negligible differences in the posterior mean or root mean square error of demographic rates among IPMs with strong dependence vs. complete independence among data sets. Our simulations suggest IPMs can be designed using only capture-recapture data or harvest and capture-recovery data where population estimates are obtained from the same data as survival and productivity data. While we encourage researchers to carefully consider the modeling approach best suited for their data sets, our results suggest that dependence among data sets does not generally compromise IPM estimates. Thus, violation of the independence assumption should not dissuade researchers from the application of IPMs in ecological research.

Interrelated impacts of climate and land-use change on a widespread waterbird

Together climate and land-use change play a crucial role in determining species distribution and abundance, but measuring the simultaneous impacts of these processes on current and future population trajectories is challenging due to time lags, interactive effects and data limitations. Most approaches that relate multiple global change drivers to population changes have been based on occurrence or count data alone. We leveraged three long-term (1995-2019) datasets to develop a coupled integrated population model-Bayesian population viability analysis (IPM-BPVA) to project future survival and reproductive success for common loons Gavia immer in northern Wisconsin, USA, by explicitly linking vital rates to changes in climate and land use. The winter North Atlantic Oscillation (NAO), a broad-scale climate index, immediately preceding the breeding season and annual changes in developed land cover within breeding areas both had strongly negative influences on adult survival. Local summer rainfall was negatively related to fecundity, though this relationship was mediated by a lagged interaction with the winter NAO, suggesting a compensatory population-level response to climate variability. We compared population viability under 12 future scenarios of annual land-use change, precipitation and NAO conditions. Under all scenarios, the loon population was expected to decline, yet the steepest declines were projected under positive NAO trends, as anticipated with ongoing climate change. Thus, loons breeding in the northern United States are likely to remain affected by climatic processes occurring thousands of miles away in the North Atlantic during the non-breeding period of the annual cycle. Our results reveal that climate and land-use changes are differentially contributing to loon population declines along the southern edge of their breeding range and will continue to do so despite natural compensatory responses. We also demonstrate that concurrent analysis of multiple data types facilitates deeper understanding of the ecological implications of anthropogenic-induced change occurring at multiple spatial scales. Our modelling approach can be used to project demographic responses of populations to varying environmental conditions while accounting for multiple sources of uncertainty, an increasingly pressing need in the face of unprecedented global change.

Decomposing fecundity and evaluating demographic influence of multiple broods in a migratory bird

Relevance of breeding season fecundity as a driver of population dynamics has been highlighted by many studies. Despite that, knowledge about how brood type specific (i.e. first, second or replacement) fecundity affects demography of multiple-brooded species is limited. In fact, estimation of brood type specific fecundity is often challenging due to imperfect detection of nesting attempts. We examined the demographic contribution and the feedback on population density of different components of fecundity, along with other vital rates, in a facultative multiple-brooded migratory bird. We used a novel formulation of a fecundity model that allows incorporating reproductive data for which information on the type of brood was unknown in some cases, and embedded it into an integrated population model (IPM) to obtain consensual estimates of all demographic rates, including brood type specific fecundities, reproductive success probabilities and proportion of breeding pairs that performed a second or replacement brood. We then conducted transient life table response experiments on IPM estimates to account for non-stationary environments. We applied the model to two 20-year datasets collected in a Swiss and a German local population of wrynecks Jynx torquilla. Brood type specific fecundities and temporal patterns of brood type specific probabilities of success, number of successful and unsuccessful first broods, probability of starting a second or a replacement brood and proportion of pairs that performed a second or a replacement brood differed between the two populations. However, changes in immigration rate and apparent survival were the dominant contributors to temporal variation and large sequential changes in realized population growth rates in both populations. In the Swiss population we also found that second brood fecundity declined when population size increased. Our study provides insight into the reproductive processes that affect population dynamics and mediate density-dependent fecundity in a migratory bird. In addition, the analytical approach proposed can be used in other studies of multiple-brooded species to maximize the use of available fecundity data through the estimation of unknown brood types, thus favouring a better understanding of the demographic contribution of brood type specific fecundity.

Climate-driven carry-over effects negatively influence population growth rate in a food-caching boreal passerine

Understanding how events throughout the annual cycle are linked is important for predicting variation in individual fitness, but whether and how carry-over effects scale up to influence population dynamics is poorly understood. Using 38 years of demographic data from Algonquin Provincial Park, Ontario, and a full annual cycle integrated population model, we examined the influence of environmental conditions and density on the population growth rate of Canada jays (Perisoreus canadensis), a resident boreal passerine that relies on perishable cached food for over-winter survival and late-winter breeding. Our results demonstrate that fall environmental variables, most notably the number of freeze-thaw events, carried over to influence late-winter fecundity, which, in turn, was the main vital rate driving population growth. These results are consistent with the hypothesis that warmer and more variable fall conditions accelerate the degradation of perishable stored food that is relied upon for successful reproduction. Future warming during the fall and winter may compromise the viability of cached food that requires consistent subzero temperatures for effective preservation, potentially exacerbating climate-driven carry-over effects that impact long-term population dynamics.

Why we should care about movements: Using spatially explicit integrated population models to assess habitat source-sink dynamics

Assessing the source–sink status of populations and habitats is of major importance for understanding population dynamics and for the management of natural populations. Sources produce a net surplus of individuals (per capita contribution to the metapopulation > 1) and will be the main contributors for self‐sustaining populations, whereas sinks produce a deficit (contribution < 1). However, making these types of assessments is generally hindered by the problem of separating mortality from permanent emigration, especially when survival probabilities as well as moved distances are habitat‐specific.

To address this long‐standing issue, we propose a spatial multi‐event integrated population model (IPM) that incorporates habitat‐specific dispersal distances of individuals. Using information about local movements, this IPM adjusts survival estimates for emigration outside the study area.

Analysing 24 years of data on a farmland passerine (the northern wheatear Oenanthe oenanthe), we assessed habitat‐specific contributions, and hence the source–sink status and temporal variation of two key breeding habitats, while accounting for habitat‐ and sex‐specific local dispersal distances of juveniles and adults. We then examined the sensitivity of the source–sink analysis by comparing results with and without accounting for these local movements.

Estimates of first‐year survival, and consequently habitat‐specific contributions, were higher when local movement data were included. The consequences from including movement data were sex specific, with contribution shifting from sink to likely source in one habitat for males, and previously noted habitat differences for females disappearing.

Assessing the source–sink status of habitats is extremely challenging. We show that our spatial IPM accounting for local movements can reduce biases in estimates of the contribution by different habitats, and thus reduce the overestimation of the occurrence of sink habitats. This approach allows combining all available data on demographic rates and movements, which will allow better assessment of source–sink dynamics and better informed conservation interventions.

Disentangling the spatial and temporal causes of decline in a bird population

The difficulties in understanding the underlying reasons of a population decline lie in the typical short duration of field studies, the often too small size already reached by a declining population or the multitude of environmental factors that may influence population trend. In this difficult context, useful demographic tools such as integrated population models (IPM) may help disentangling the main reasons for a population decline. To understand why a hoopoe Upupa epops population has declined, we followed a three step model analysis. We built an IPM structured with respect to habitat quality (approximated by the expected availability of mole crickets, the main prey in our population) and estimated the contributions of habitat-specific demographic rates to population variation and decline. We quantified how much each demographic rate has decreased and investigated whether habitat quality influenced this decline. We tested how much weather conditions and research activities contributed to the decrease in the different demographic rates. The decline of the hoopoe population was mainly explained by a decrease in first-year apparent survival and a reduced number of fledglings produced, particularly in habitats of high quality. Since a majority of pairs bred in habitats of the highest quality, the decrease in the production of locally recruited yearlings in high-quality habitat was the main driver of the population decline despite a homogeneous drop of recruitment across habitats. Overall, the explanatory variables we tested only accounted for 19% of the decrease in the population growth rate. Among these variables, the effects of spring temperature (49% of the explained variance) contributed more to population decline than spring precipitation (36%) and research activities (maternal capture delay, 15%). This study shows the power of IPMs for identifying the vital rates involved in population declines and thus paves the way for targeted conservation and management actions.

Integrating broad-scale data to assess demographic and climatic contributions to population change in a declining songbird

Climate variation and trends affect species distribution and abundance across large spatial extents. However, most studies that predict species response to climate are implemented at small spatial scales or are based on occurrence-environment relationships that lack mechanistic detail. Here, we develop an integrated population model (IPM) for multi-site count and capture-recapture data for a declining migratory songbird, Wilson's warbler (Cardellina pusilla), in three genetically distinct breeding populations in western North America. We include climate covariates of vital rates, including spring temperatures on the breeding grounds, drought on the wintering range in northwest Mexico, and wind conditions during spring migration. Spring temperatures were positively related to productivity in Sierra Nevada and Pacific Northwest genetic groups, and annual changes in productivity were important predictors of changes in growth rate in these populations. Drought condition on the wintering grounds was a strong predictor of adult survival for coastal California and Sierra Nevada populations; however, adult survival played a relatively minor role in explaining annual variation in population change. A latent parameter representing a mixture of first-year survival and immigration was the largest contributor to variation in population change; however, this parameter was estimated imprecisely, and its importance likely reflects, in part, differences in spatio-temporal distribution of samples between count and capture-recapture data sets. Our modeling approach represents a novel and flexible framework for linking broad-scale multi-site monitoring data sets. Our results highlight both the potential of the approach for extension to additional species and systems, as well as needs for additional data and/or model development.

Incorporating citizen science data in spatially explicit integrated population models

Information about population abundance, distribution, and demographic rates is critical for understanding a species' ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence-absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture-recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi-season simulations with parameters informed by North American black bear (Ursus americanus) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population-level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost-efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.

Estimability of migration survival rates from integrated breeding and winter capture-recapture data

Long-distance migration is a common phenomenon across the animal kingdom but the scale of annual migratory movements has made it difficult for researchers to estimate survival rates during these periods of the annual cycle. Estimating migration survival is particularly challenging for small-bodied species that cannot carry satellite tags, a group that includes the vast majority of migratory species. When capture-recapture data are available for linked breeding and non-breeding populations, estimation of overall migration survival is possible but current methods do not allow separate estimation of spring and autumn survival rates. Recent development of a Bayesian integrated survival model has provided a method to separately estimate the latent spring and autumn survival rates using capture-recapture data, though the accuracy and precision of these estimates has not been formally tested. Here, I used simulated data to explore the estimability of migration survival rates using this model. Under a variety of biologically realistic scenarios, I demonstrate that spring and autumn migration survival can be estimated from the integrated survival model, though estimates are biased toward the overall migration survival probability. The direction and magnitude of this bias are influenced by the relative difference in spring and autumn survival rates as well as the degree of annual variation in these rates. The inclusion of covariates can improve the model's performance, especially when annual variation in migration survival rates is low. Migration survival rates can be estimated from relatively short time series (4-5 years), but bias and precision of estimates are improved when longer time series (10-12 years) are available. The ability to estimate seasonal survival rates of small, migratory organisms opens the door to advancing our understanding of the ecology and conservation of these species. Application of this method will enable researchers to better understand when mortality occurs across the annual cycle and how the migratory periods contribute to population dynamics. Integrating summer and winter capture data requires knowledge of the migratory connectivity of sampled populations and therefore efforts to simultaneously collect both survival and tracking data should be a high priority, especially for species of conservation concern.

Quantifying the links between land use and population growth rate in a declining farmland bird

Land use is likely to be a key driver of population dynamics of species inhabiting anthropogenic landscapes, such as farmlands. Understanding the relationships between land use and variation in population growth rates is therefore critical for the management of many farmland species. Using 24 years of data of a declining farmland bird in an integrated population model, we examined how spatiotemporal variation in land use (defined as habitats with "Short" and "Tall" ground vegetation during the breeding season) and habitat-specific demographic parameters relates to variation in population growth taking into account individual movements between habitats. We also evaluated contributions to population growth using transient life table response experiments which gives information on contribution of past variation of parameters and real-time elasticities which suggests future scenarios to change growth rates. LTRE analyses revealed a clear contribution of Short habitats to the annual variation in population growth rate that was mostly due to fledgling recruitment, whereas there was no evidence for a contribution of Tall habitats. Only 18% of the variation in population growth was explained by the modeled local demography, the remaining variation being explained by apparent immigration (i.e., the residual variation). We discuss potential biological and methodological reasons for high contributions of apparent immigration in open populations. In line with LTRE analysis, real-time elasticity analysis revealed that demographic parameters linked to Short habitats had a stronger potential to influence population growth rate than those of Tall habitats. Most particularly, an increase of the proportion of Short sites occupied by Old breeders could have a distinct positive impact on population growth. High-quality Short habitats such as grazed pastures have been declining in southern Sweden. Converting low-quality to high-quality habitats could therefore change the present negative population trend of this, and other species with similar habitat requirements.

Immature survival, fertility, and density dependence drive global population dynamics in a long-lived species

Disentangling the influence of demographic parameters and the role of density dependence on species' population dynamics is a challenge, especially when fractions of the population are unobservable. Additionally, due to the difficulty of gathering data at large spatial scales, most studies ignore the global dynamic of a species, which would integrate local heterogeneity dynamics and remove the noise of dispersal. We developed an integrated population model (IPM) at a global scale to disentangle the main demographic drivers of population dynamics in a long-lived species. We used 28 yr of Audouin's Gull demographic data encompassing 69 local patches (comprising 90% of the world population). Importantly, we took into account the unobservable fraction of non-breeders and also assessed the strength of density dependence for this fraction of the population. As predicted by life histories of long-lived organisms, temporal random variation in survival was highest for immature individuals (1.326, 95% credible interval [CRI] 1.290-1.940) and lowest for adults (0.499, 95% CRI 0.487-0.720). Large temporal fluctuations in the probability of taking a reproductive sabbatical would partly explain the consistency in adult survival, with individuals most likely refraining from breeding when environmental conditions were harsh. Immature survival and fertility were the main drivers of population dynamics during the study period (r²  = 0.83, 0.77-0.87 and 0.73, 0.63-0.79, respectively). We found strong evidence of density dependence, not only due to the number of breeders (r²  = -0.34, -0.43 to -0.24) but also due to individuals on sabbatical (r²  = -0.18, -0.33 to -0.01). From a conservation point of view, the species shows a 5% annual global decrease during the last 10 years, and we propose an update of its conservation status. Even though population dynamics of long-lived organisms are very sensitive to changes in adult survival, we show here that, in the absence of strong environmental perturbations affecting this vital rate, fluctuations in population density are mainly driven by variations in survival of immature individuals and fertility. Integrated models based on long-term monitoring at a global scale may enhance our ecological and evolutionary understanding of how demographic drivers influence population dynamics.

Density dependence in an age-structured population of great tits: identifying the critical age classes

Classical approaches for the analyses of density dependence assume that all the individuals in a population equally respond and equally contribute to density dependence. However, in age-structured populations, individuals of different ages may differ in their responses to changes in population size and how they contribute to density dependence affecting the growth rate of the whole population. Here we apply the concept of critical age classes, i.e., a specific scalar function that describes how one or a combination of several age classes affect the demographic rates negatively, in order to examine how total density dependence acting on the population growth rate depends on the age-specific population sizes. In a 38-yr dataset of an age-structured great tit (Parus major) population, we find that the age classes, including the youngest breeding females, were the critical age classes for density regulation. These age classes correspond to new breeders that attempt to take a territory and that have the strongest competitive effect on other breeding females. They strongly affected population growth rate and reduced recruitment and survival rates of all breeding females. We also show that depending on their age class, females may differently respond to varying density. In particular, the negative effect of the number of breeding females was stronger on recruitment rate of the youngest breeding females. These findings question the classical assumptions that all the individuals of a population can be treated as having an equal contribution to density regulation and that the effect of the number of individuals is age independent. Our results improve our understanding of density regulation in natural populations.

Understanding the demographic drivers of realized population growth rates

Identifying the demographic parameters (e.g., reproduction, survival, dispersal) that most influence population dynamics can increase conservation effectiveness and enhance ecological understanding. Life table response experiments (LTRE) aim to decompose the effects of change in parameters on past demographic outcomes (e.g., population growth rates). But the vast majority of LTREs and other retrospective population analyses have focused on decomposing asymptotic population growth rates, which do not account for the dynamic interplay between population structure and vital rates that shape realized population growth rates (λt=Nt+1/Nt) in time-varying environments. We provide an empirical means to overcome these shortcomings by merging recently developed "transient life-table response experiments" with integrated population models (IPMs). IPMs allow for the estimation of latent population structure and other demographic parameters that are required for transient LTRE analysis, and Bayesian versions additionally allow for complete error propagation from the estimation of demographic parameters to derivations of realized population growth rates and perturbation analyses of growth rates. By integrating available monitoring data for Lesser Scaup over 60 yr, and conducting transient LTREs on IPM estimates, we found that the contribution of juvenile female survival to long-term variation in realized population growth rates was 1.6 and 3.7 times larger than that of adult female survival and fecundity, respectively. But a persistent long-term decline in fecundity explained 92% of the decline in abundance between 1983 and 2006. In contrast, an improvement in adult female survival drove the modest recovery in Lesser Scaup abundance since 2006, indicating that the most important demographic drivers of Lesser Scaup population dynamics are temporally dynamic. In addition to resolving uncertainty about Lesser Scaup population dynamics, the merger of IPMs with transient LTREs will strengthen our understanding of demography for many species as we aim to conserve biodiversity during an era of non-stationary global change.

Spatial and temporal drivers of avian population dynamics across the annual cycle

Untangling the spatial and temporal processes that influence population dynamics of migratory species is challenging, because changes in abundance are shaped by variation in vital rates across heterogeneous habitats and throughout the annual cycle. We developed a full-annual-cycle, integrated, population model and used demographic data collected between 2011 and 2014 in southern Indiana and Belize to estimate stage-specific vital rates of a declining migratory songbird, the Wood Thrush (Hylocichla mustelina). Our primary objective was to understand how spatial and temporal variation in demography contributes to local and regional population growth. Our full-annual-cycle model allowed us to estimate (1) age-specific, seasonal survival probabilities, including latent survival during both spring and autumn migration, and (2) how the relative contribution of vital rates to population growth differed among habitats. Wood Thrushes in our study populations experienced the lowest apparent survival rates during migration and apparent survival was lower during spring migration than during fall migration. Both mortality and high dispersal likely contributed to low apparent survival during spring migration. Population growth in high-quality habitat was most sensitive to variation in fecundity and apparent survival of juveniles during spring migration, whereas population growth in low-quality sites was most sensitive to adult apparent breeding-season survival. These results elucidate how full-annual-cycle vital rates, particularly apparent survival during migration, interact with spatial variation in habitat quality to influence population dynamics in migratory species.

Integrated population modeling reveals the impact of climate on the survival of juvenile emperor penguins

Early-life demographic traits are poorly known, impeding our understanding of population processes and sensitivity to climate change. Survival of immature individuals is a critical component of population dynamics and recruitment in particular. However, obtaining reliable estimates of juvenile survival (i.e., from independence to first year) remains challenging, as immatures are often difficult to observe and to monitor individually in the field. This is particularly acute for seabirds, in which juveniles stay at sea and remain undetectable for several years. In this work, we developed a Bayesian integrated population model to estimate the juvenile survival of emperor penguins (Aptenodytes forsteri), and other demographic parameters including adult survival and fecundity of the species. Using this statistical method, we simultaneously analyzed capture-recapture data of adults, the annual number of breeding females, and the number of fledglings of emperor penguins collected at Dumont d'Urville, Antarctica, for the period 1971-1998. We also assessed how climate covariates known to affect the species foraging habitats and prey [southern annular mode (SAM), sea ice concentration (SIC)] affect juvenile survival. Our analyses revealed that there was a strong evidence for the positive effect of SAM during the rearing period (SAMR) on juvenile survival. Our findings suggest that this large-scale climate index affects juvenile emperor penguins body condition and survival through its influence on wind patterns, fast ice extent, and distance to open water. Estimating the influence of environmental covariates on juvenile survival is of major importance to understand the impacts of climate variability and change on the population dynamics of emperor penguins and seabirds in general and to make robust predictions on the impact of climate change on marine predators.

Immigration ensures population survival in the Siberian flying squirrel

Linking dispersal to population growth remains a challenging task and is a major knowledge gap, for example, for conservation management. We studied relative roles of different demographic rates behind population growth in Siberian flying squirrels in two nest-box breeding populations in western Finland. Adults and offspring were captured and individually identifiable. We constructed an integrated population model, which estimated all relevant annual demographic rates (birth, local [apparent] survival, and immigration) as well as population growth rates. One population (studied 2002-2014) fluctuated around a steady-state equilibrium, whereas the other (studied 1995-2014) showed a numerical decline. Immigration was the demographic rate which showed clear correlations to annual population growth rates in both populations. Population growth rate was density dependent in both populations. None of the demographic rates nor the population growth rate correlated across the two study populations, despite their proximity suggesting that factors regulating the dynamics are determined locally. We conclude that flying squirrels may persist in a network of uncoupled subpopulations, where movement between subpopulations is of critical importance. Our study supports the view that dispersal has the key role in population survival of a small forest rodent.

An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics

Ecological invasions and colonizations occur dynamically through space and time. Estimating the distribution and abundance of colonizing species is critical for efficient management or conservation. We describe a statistical framework for simultaneously estimating spatiotemporal occupancy and abundance dynamics of a colonizing species. Our method accounts for several issues that are common when modeling spatiotemporal ecological data including multiple levels of detection probability, multiple data sources, and computational limitations that occur when making fine-scale inference over a large spatiotemporal domain. We apply the model to estimate the colonization dynamics of sea otters (Enhydra lutris) in Glacier Bay, in southeastern Alaska.

Evaluating mortality rates with a novel integrated framework for nonmonogamous species

The conservation of wildlife requires management based on quantitative evidence, and especially for large carnivores, unraveling cause-specific mortalities and understanding their impact on population dynamics is crucial. Acquiring this knowledge is challenging because it is difficult to obtain robust long-term data sets on endangered populations and, usually, data are collected through diverse sampling strategies. Integrated population models (IPMs) offer a way to integrate data generated through different processes. However, IPMs are female-based models that cannot account for mate availability, and this feature limits their applicability to monogamous species only. We extended classical IPMs to a two-sex framework that allows investigation of population dynamics and quantification of cause-specific mortality rates in nonmonogamous species. We illustrated our approach by simultaneously modeling different types of data from a reintroduced, unhunted brown bear (Ursus arctos) population living in an area with a dense human population. In a population mainly driven by adult survival, we estimated that on average 11% of cubs and 61% of adults died from human-related causes. Although the population is currently not at risk, adult survival and thus population dynamics are driven by anthropogenic mortality. Given the recent increase of human-bear conflicts in the area, removal of individuals for management purposes and through poaching may increase, reversing the positive population growth rate. Our approach can be generalized to other species affected by cause-specific mortality and will be useful to inform conservation decisions for other nonmonogamous species, such as most large carnivores, for which data are scarce and diverse and thus data integration is highly desirable.

Disease introduction is associated with a phase transition in bighorn sheep demographics

Ecological theory suggests that pathogens are capable of regulating or limiting host population dynamics, and this relationship has been empirically established in several settings. However, although studies of childhood diseases were integral to the development of disease ecology, few studies show population limitation by a disease affecting juveniles. Here, we present empirical evidence that disease in lambs constrains population growth in bighorn sheep (Ovis canadensis) based on 45 years of population-level and 18 years of individual-level monitoring across 12 populations. While populations generally increased (λ = 1.11) prior to disease introduction, most of these same populations experienced an abrupt change in trajectory at the time of disease invasion, usually followed by stagnant-to-declining growth rates (λ = 0.98) over the next 20 years. Disease-induced juvenile mortality imposed strong constraints on population growth that were not observed prior to disease introduction, even as adult survival returned to pre-invasion levels. Simulations suggested that models including persistent disease-induced mortality in juveniles qualitatively matched observed population trajectories, whereas models that only incorporated all-age disease events did not. We use these results to argue that pathogen persistence may pose a lasting, but under-recognized, threat to host populations, particularly in cases where clinical disease manifests primarily in juveniles.

Spatial heterogeneity in the carrying capacity of sika deer in Japan

Carrying capacity is 1 driver of wildlife population dynamics. Although in previous studies carrying capacity was considered to be a fixed entity, it may differ among locations due to environmental variation. The factors underlying variability in carrying capacity, however, have rarely been examined. Here, we investigated spatial heterogeneity in the carrying capacity of Japanese sika deer ( Cervus nippon ) from 2005 to 2014 in Yamanashi Prefecture, central Japan (mesh with grid cells of 5.5×4.6 km) by state-space modeling. Both carrying capacity and density dependence differed greatly among cells. Estimated carrying capacities ranged from 1.34 to 98.4 deer/km ² . According to estimated population dynamics, grid cells with larger proportions of artificial grassland and deciduous forest were subject to lower density dependence and higher carrying capacity. We conclude that population dynamics of ungulates may vary spatially through spatial variation in carrying capacity and that the density level for controlling ungulate abundance should be based on the current density level relative to the carrying capacity for each area.

Differential contribution of demographic rate synchrony to population synchrony in barn swallows

Populations of many species show temporally synchronous dynamics over some range, mostly caused by spatial autocorrelation of the environment that affects demographic rates. Synchronous fluctuation of a demographic rate is a necessary, but not sufficient condition for population synchrony because population growth is differentially sensitive to variation in demographic rates. Little is known about the relative effects of demographic rates to population synchrony, because it is rare that all demographic rates from several populations are known. We develop a hierarchical integrated population model with which all relevant demographic rates from all study populations can be estimated and apply it to demographic data of barn swallows Hirundo rustica from nine sites that were between 19 and 224 km apart from each other. We decompose the variation of the population growth and of the demographic rates (apparent survival, components of productivity, immigration) into global and local temporal components using random effects which allowed the estimation of synchrony of these rates. The barn swallow populations fluctuated synchronously, but less so than most demographic rates. The highest synchrony showed the probability of double brooding, while fledging success was highly asynchronous. Apparent survival, immigration and total productivity achieved intermediate levels of synchrony. The growth of all populations was most sensitive to changes in immigration and adult apparent survival, and both of them contributed to the observed temporal variation of population growth rates. Using a simulation model, we show that immigration and apparent survival of juveniles and adults were able to induce population synchrony, but not components of local productivity due to their low population growth rate sensitivity. Immigrants are mostly first-time breeders, and consequently, their number depends on the productivity of neighbouring populations. Since total productivity was synchronized, we conclude that it contributed to population synchrony in an indirect way through dispersing individuals which appear as immigrants at the local scale. The hierarchical integrated population model is promising for achieving an improved mechanistic understanding of population synchrony.

Are the numbers adding up? Exploiting discrepancies among complementary population models

Large carnivores are difficult to monitor because they tend to be sparsely distributed, sensitive to human activity, and associated with complex life histories. Consequently, understanding population trend and viability requires conservationists to cope with uncertainty and bias in population data. Joint analysis of combined data sets using multiple models (i.e., integrated population model) can improve inference about mechanisms (e.g., habitat heterogeneity and food distribution) affecting population dynamics. However, unobserved or unobservable processes can also introduce bias and can be difficult to quantify. We developed a Bayesian hierarchical modeling approach for inference on an integrated population model that reconciles annual population counts with recruitment and survival data (i.e., demographic processes). Our modeling framework is flexible and enables a realistic form of population dynamics by fitting separate density-dependent responses for each demographic process. Discrepancies estimated from shared parameters among different model components represent unobserved additions (i.e., recruitment or immigration) or removals (i.e., death or emigration) when annual population counts are reliable. In a case study of gray wolves in Wisconsin (1980–2011), concordant with policy changes, we estimated that a discrepancy of 0% (1980–1995), −2% (1996–2002), and 4% (2003–2011) in the annual mortality rate was needed to explain annual growth rate. Additional mortality in 2003–2011 may reflect density-dependent mechanisms, changes in illegal killing with shifts in wolf management, and nonindependent censoring in survival data. Integrated population models provide insights into unobserved or unobservable processes by quantifying discrepancies among data sets. Our modeling approach is generalizable to many population analysis needs and allows for identifying dynamic differences due to external drivers, such as management or policy changes.

An integrated modeling approach to estimating Gunnison sage-grouse population dynamics: combining index and demographic data

Evaluation of population dynamics for rare and declining species is often limited to data that are sparse and/or of poor quality. Frequently, the best data available for rare bird species are based on large-scale, population count data. These data are commonly based on sampling methods that lack consistent sampling effort, do not account for detectability, and are complicated by observer bias. For some species, short-term studies of demographic rates have been conducted as well, but the data from such studies are typically analyzed separately. To utilize the strengths and minimize the weaknesses of these two data types, we developed a novel Bayesian integrated model that links population count data and population demographic data through population growth rate (λ) for Gunnison sage-grouse (Centrocercus minimus). The long-term population index data available for Gunnison sage-grouse are annual (years 1953–2012) male lek counts. An intensive demographic study was also conducted from years 2005 to 2010. We were able to reduce the variability in expected population growth rates across time, while correcting for potential small sample size bias in the demographic data. We found the population of Gunnison sage-grouse to be variable and slightly declining over the past 16 years.