Quantcast


CURATOR
A pinboard by
Fraser Combe
PINBOARD SUMMARY

Collection of papers for my project on integrated population modelling (IPM)

Collection of papers on ipm a novel method for estimating population parameters improving precision of estimates. This is a key collection of the important papers I have referenced in my most recent manuscript

11 ITEMS PINNED

High survival during hibernation affects onset and timing of reproduction.

Abstract: The timing of reproduction is one of the most crucial life history traits, with enormous consequences for the fitness of an individual. We investigated the effects of season and timing of birth on local survival probability in a small mammalian hibernator, the common dormouse (Muscardinus avellanarius). Local monthly survival probability was lowest in the early active season (May-August, ϕ(adult) = 0.75-0.88, ϕ(juvenile) = 0.61-0.68), increased during the late active season (August-October), and highest during hibernation (October-May, ϕ(adult) = 0.96-0.98, ϕ(juvenile) = 0.81-0.94). Consequently, dormice had an extremely high winter survival probability. We observed two peaks in the timing of reproduction (June and August/September, respectively), with the majority of juveniles born late in the active season. Although early investment in reproduction seems the better life history tactic [survival probability until onset of reproduction: ϕ(born early) = 0.46, 95% confidence interval (CI) 0.28-0.64; ϕ(born late) = 0.19, 95% CI = 0.09-0.28], only females with a good body condition (significantly higher body mass) invest in reproduction early in the year. We suggest the high over-winter survival in dormice allows for a unique life history pattern (i.e., combining slow and fast life history tactics), which leads to a bimodal seasonal birth pattern: (1) give birth as early as possible to allow even the young to breed before hibernating, and/or (2) give birth as late as possible (leaving just enough time for these young to fatten) and enter directly into a period associated with the highest survival rates (hibernation) until maturity.

Pub.: 19 Nov '11, Pinned: 12 Sep '17

Summer mortality in the hazel dormouse (Muscardinus avellanarius) and its effect on population dynamics

Abstract: In the period 2000–2012, 38.3 % of 950 marked overwintered hazel dormice (Muscardinus avellanarius) were not recaptured at a study site in Lithuania in autumn. As adult dormice are sedentary, it is presumed that those dormice not recaptured died between late April and August. The highest total number of dormice captured for the last time was recorded in May and the lowest in August. The total summer mortality was significantly higher in females (42.5 %) than in males (34.6 %), but it did not depend on dormouse age or body weight. Tawny owl (Strix aluco) is the main known dormouse predator in Lithuania, and likely, it has the highest impact on summer mortality of M. avellanarius. Over the years, the total summer mortality of adult dormice ranged from 27 % to 52 %. The increased summer mortality resulted in decreased total dormouse population density and particularly decreased density of adult females in summer. Decreased densities led to more intensive breeding in the dormouse population, specifically breeding by young-of-the-year females, a pattern that is not common for this species. The number of breeding cases by young-of-the-year females was inversely related to the density of adult overwintered females in summer and to the number of breeding cases of these females. Breeding by young-of-the-year females was the main factor in the restoration of decreased population density in summer. Lithuanian populations of M. avellanarius are unique in their high proportion of breeding cases by young-of-the-year females amongst all populations investigated in the entire species distributional range.

Pub.: 09 Jul '13, Pinned: 12 Sep '17

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

Abstract: 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.

Pub.: 19 Feb '15, Pinned: 12 Sep '17

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

Abstract: 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.

Pub.: 17 Jul '15, Pinned: 12 Sep '17

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

Abstract: 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. This article is protected by copyright. All rights reserved.

Pub.: 23 Oct '16, Pinned: 12 Sep '17

An integrated population model for bird monitoring in North America.

Abstract: Integrated population models (IPMs) provide a unified framework for simultaneously analyzing data sets of different types to estimate vital rates, population size, and dynamics; assess contributions of demographic parameters to population changes; and assess population viability. Strengths of an IPM include the ability to estimate latent parameters and improve the precision of parameter estimates. We present a hierarchical IPM that combines two broad-scale avian monitoring data sets; count data from the North American Breeding Bird Survey (BBS) and capture-recapture data from the Monitoring Avian Productivity and Survivorship (MAPS) program. These data sets are characterized by large numbers of sample sites and observers, factors capable of inducing error in the sampling and observation processes. The IPM integrates the data sets by modeling the population abundance as a first-order autoregressive function of the previous year's population abundance and vital rates. BBS counts were modeled as a log-linear function of the annual index of population abundance, observation effects (observer identity and first-survey-year), and overdispersion. Vital rates modeled included adult apparent survival, estimated from a transient Cormack-Jolly-Seber model using MAPS data, and recruitment (surviving hatched birds from the previous season + dispersing adults) estimated as a latent parameter. An assessment of the IPM demonstrated it could recover true parameter values from 200 simulated data sets. The IPM was applied to data sets (1992-2008) of two bird species, gray catbird (Dumetella carolinensis) and wood thrush (Hylocichla mustelina) in the New England/Mid-Atlantic coastal Bird Conservation Region of the USA. The gray catbird population was relatively stable (trend 0.4% yr(-1) ), while the wood thrush population nearly halved (trend -4.5% yr(-1) ) over the 17-yr study period. IPM estimates of population growth rates, adult survival, and detection and residency probabilities were similar and as precise as estimates from the stand-alone BBS and CJS models. A benefit of using the IPM was its ability to estimate the latent recruitment parameter. Annual growth rates for both species correlated more with recruitment than survival, and the relationship for wood thrush was stronger than for gray catbird. The IPM's unified modeling framework facilitates integration of these important data sets. This article is protected by copyright. All rights reserved.

Pub.: 31 Dec '16, Pinned: 12 Sep '17

Assessing the importance of demographic parameters for population dynamics using Bayesian integrated population modeling.

Abstract: 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 population growth rate 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 days 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. This article is protected by copyright. All rights reserved.

Pub.: 12 Feb '17, Pinned: 12 Sep '17