Yellow-Bellied Marmot Population Dynamics: Demographic Mechanisms of Growth and Decline

Oli, Madan K.; Armitage, Kenneth B.

doi:10.1890/03-0513

Cited by 46 publications

(61 citation statements)

References 48 publications

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“…Theory predicts that demographic parameters to which population growth rate is highly sensitive should be less variable than those to which growth rate is less sensitive (Cairns 1992;Gaillard et al 1998Gaillard et al , 2000Pfister 1998). Oli and Armitage (2004) found that asymptotic growth rate of a yellow-bellied marmot population was generally more sensitive to changes in survival parameters than to reproductive parameters. Thus, we expected survival parameters to be less variable over space or time than reproductive parameters.…”

Section: Introductionmentioning

confidence: 95%

“…Reproduction is an important life history trait that can be particularly sensitive to spatiotemporal variation in the environment (Caswell 2001;Coulson et al 1999Coulson et al , 2000Heppell et al 2000). Because population growth rates are highly sensitive to changes in reproductive parameters in many species (e.g., Oli and Armitage 2004;Oli and Dobson 2003;Saether and Bakke 2000), spatiotemporal variation in these rates can influence the dynamics and persistence of populations. Therefore, a thorough understanding of population dynamics requires a detailed Communicated by John Reeve.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal variation in reproductive parameters of yellow-bellied marmots

et al. 2007

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Spatiotemporal variation in reproductive rates is a common phenomenon in many wildlife populations, but the population dynamic consequences of spatial and temporal variability in different components of reproduction remain poorly understood. We used 43 years (1962-2004) of data from 17 locations and a capture-mark-recapture (CMR) modeling framework to investigate the spatiotemporal variation in reproductive parameters of yellow-bellied marmots (Marmota flaviventris), and its influence on the realized population growth rate. Specifically, we estimated and modeled breeding probabilities of two-year-old females (earliest age of first reproduction), >2-year-old females that have not reproduced before (subadults), and >2-year-old females that have reproduced before (adults), as well as the litter sizes of two-year old and >2-year-old females. Most reproductive parameters exhibited spatial and/or temporal variation. However, reproductive parameters differed with respect to their relative influence on the realized population growth rate (lambda). Litter size had a stronger influence than did breeding probabilities on both spatial and temporal variations in lambda. Our analysis indicated that lambda was proportionately more sensitive to survival than recruitment. However, the annual fluctuation in litter size, abetted by the breeding probabilities, accounted for most of the temporal variation in lambda.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal variation in reproductive parameters of yellow-bellied marmots

et al. 2007

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“…Retrospective perturbation analyses, such as life table response experiments (LTRE), allow scientists to evaluate how variation in specific vital rates contributed to past population dynamics (Caswell 2000, Oli andArmitage 2004) whereas prospective perturbation analyses, such as sensitivity and elasticity analyses, quantitate how equivalent changes in vital rates would affect future population growth. Collectively, these demographic tools can provide critical information about the ecology and life history of a species, prioritize research and conservation actions, and help develop species recovery plans (Crowder et al 1994, Doak et al 1994, Caswell 2001.…”

Section: Introductionmentioning

confidence: 99%

Evaluating vital rate contributions to greater sage‐grouse population dynamics to inform conservation

et al. 2016

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Abstract. Species conservation efforts often use short-term studies that fail to identify the vital rates that contribute most to population growth. Although the greater sage-grouse (Centrocercus urophasianus; sage-grouse) is a candidate for protection under the U.S. Endangered Species Act, and is sometimes referred to as an umbrella species in the sagebrush (Artemisia spp.) biome of western North America, the failure of proposed management strategies to focus on key vital rates that may contribute most to achieving population stability remains problematic for sustainable conservation. To address this dilemma, we performed both prospective and retrospective perturbation analyses of a life cycle model based on a 12-yr study that encompassed nearly all sage-grouse vital rates. To validate our population models, we compared estimates of annual finite population growth rates (λ) from our female-based life cycle models to those attained from male-based lek counts. Post-fledging (i.e., after second year, second year, and juvenile) female survival parameters contributed most to past variation in λ during our study and had the greatest potential to change λ in the future, indicating these vital rates as important determinants of sage-grouse population dynamics. In addition, annual estimates of λ from female-based life cycle models and malebased lek data were similar, providing the most rigorous evidence to date that lek counts of males can serve as a valid index of sage-grouse population change. Our comparison of fixed and mixed statistical models for evaluating temporal variation in nest survival and initiation suggest that conservation planners use caution when evaluating short-term nesting studies and using associated fixed-effect results to develop conservation objectives. In addition, our findings indicated that greater attention should be paid to those factors affecting sage-grouse post-fledging females. Our approach demonstrates the need for more long-term studies of species vital rates across the life cycle. Such studies should address the decoupling of sampling variation from underlying process (co)variation in vital rates, identification of how such variation drives population dynamics, and how decision makers can use this information to re-direct conservation efforts to address the most limiting points in the life cycle for a given population.

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“…After a rather heated debate about the relative roles of exogenous (density-independent, Andrewartha and Birch 1954) versus endogenous (densitydependent, Nicholson 1957) factors influencing population dynamics, there is now broad consensus that both factors are important (Leirs et al 1997;Grenfell et al 1998;Coulson et al 2001Coulson et al , 2004aAars and Ims 2002;Ozgul et al 2004;Oli and Armitage 2004). Whereas density dependence is considered a ubiquitous feature of the dynamics of many populations (May 1973;Fryxell et al 1991;Royama 1992;Forchhammer et al 1998), recent work has emphasised that the interaction between a density-dependent ''skeleton'' and a stochastic ''noise'' component can enhance our understanding of population dynamics (Leirs et al 1997;Cushing 1998;Krüger et al 2002;Coulson et al 2004a).…”

Section: Introductionmentioning

confidence: 99%

“…Hence, a link between individual life histories, population demography and population dynamics might O. Krüger (&) Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK e-mail: ok212@cam.ac.uk prove fruitful in generating a deeper understanding of the causes and mechanisms (de Kroon et al 2000;Oli and Armitage 2004), because individual fitness properties are bound to have consequences at population level (Sutherland 1996). Such a link necessitates long-term, individual-based studies, which have recently emerged in comparative abundance (e.g.…”

Section: Introductionmentioning

confidence: 99%

Long-term demographic analysis in goshawk Accipiter gentilis: the role of density dependence and stochasticity

Krüger

2007

Oecologia

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Density dependence and environmental stochasticity are both potentially important processes influencing population demography and long-term population growth. Quantifying the importance of these two processes for population growth requires both long-term population as well as individual-based data. I use a 30-year data set on a goshawk Accipiter gentilis population from Eastern Westphalia, Germany, to describe the key vital rate elements to which the growth rate is most sensitive and test how environmental stochasticity and density dependence affect long-term population growth. The asymptotic growth rate of the fully age-structured mean matrix model was very similar to the observed one (0.7% vs. 0.3% per annum), and population growth was most elastic to changes in survival rate at age classes 1-3. Environmental stochasticity led only to a small change in the projected population growth rate (between -0.16% and 0.67%) and did not change the elasticities qualitatively, suggesting that the goshawk life history of early reproduction coupled with high annual fertility buffers against a variable environment. Age classes most crucial to population growth were those in which density dependence seemed to act most strongly. This emphasises the importance of density dependence as a regulatory mechanism in this goshawk population. It also provides a mechanism that might enable the population to recover from population lows, because a mean matrix model incorporating observed functional responses of both vital rates to population density coupled with environmental stochasticity reduced long-term extinction risk of 30% under density-independent environmental stochasticity and 60% under demographic stochasticity to zero.

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Yellow-Bellied Marmot Population Dynamics: Demographic Mechanisms of Growth and Decline

Cited by 46 publications

References 48 publications

Spatiotemporal variation in reproductive parameters of yellow-bellied marmots

Spatiotemporal variation in reproductive parameters of yellow-bellied marmots

Evaluating vital rate contributions to greater sage‐grouse population dynamics to inform conservation

Long-term demographic analysis in goshawk Accipiter gentilis: the role of density dependence and stochasticity

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