Match!

Stochastic effects contribute to population fitness differences

Published on Sep 1, 2019in Ecological Modelling2.634
· DOI :10.1016/j.ecolmodel.2019.108760
Raziel Davison5
Estimated H-index: 5
(UCSB: University of California, Santa Barbara),
Marc Stadman (Radboud University Nijmegen), Eelke Jongejans30
Estimated H-index: 30
(Radboud University Nijmegen)
Abstract
Abstract Demographic rates differ between populations and also fluctuate over time, sometimes driving large fitness differences, but the strength of stochastic effects remain heretofore unresolved. We demonstrate the importance of stochastic processes by comparing the drivers of long-term population growth. We quantify stochastic contributions to differences in population growth rates among 218 plant and two animal populations representing 62 species (all records from the COMPADRE and COMADRE matrix databases suitable for our analyses) using the Small Noise Approximation Life Table Response Experiment ( SNA-LTRE ), a recently developed matrix model tool for decomposing the stochastic contributions of elasticities, variability and correlations. Stochastic influences comprise over a quarter of all contributions to population growth variation among populations (mean ± SD = 28 ± 14%). The relative importance of stochastic effects decreases with generation time and lifespan, confirming predictions that longevity buffers populations against the negative effects of variability. Stochastic effects are larger when populations differ widely in growth rates, suggesting that stochasticity is likely to be important where ecological conditions vary greatly, and are larger among herbaceous perennials than among woody plants, ferns and succulents, possibly reflecting phenotypic plasticity in response to fluctuating environments. Overall, we show that stochastic effects are often strong enough to warrant the additional effort required to characterize their contributions to population growth.
  • References (85)
  • Citations (0)
📖 Papers frequently viewed together
99 Citations
9 Citations
20134.29Ecology
22 Citations
78% of Scinapse members use related papers. After signing in, all features are FREE.
References85
Newest
#1Maria Paniw (UCA: University of Cádiz)H-Index: 6
#2Arpat Ozgul (UZH: University of Zurich)H-Index: 22
Last. Roberto Salguero-GómezH-Index: 20
view all 3 authors...
Temporal autocorrelation in demographic processes is an important aspect of population dynamics, but a comprehensive examination of its effects on different life-history strategies is lacking. We use matrix population models from 454 plant and animal populations to simulate stochastic population growth rates (log lambda(s)) under different temporal autocorrelations in demographic rates, using simulated and observed covariation among rates. We then test for differences in sensitivities, or change...
13 CitationsSource
#1Aldo Compagnoni (Rice University)H-Index: 6
#2Andrew J. Bibian (Rice University)H-Index: 2
Last. Tom E. X. Miller (Rice University)H-Index: 23
view all 11 authors...
Understanding the influence of environmental variability on population dynamics is a fundamental goal of ecology. Theory suggests that, for populations in variable environments, temporal correlations between demographic vital rates (e.g., growth, survival, reproduction) can increase (if positive) or decrease (if negative) the variability of year-to-year population growth. Because this variability generally decreases long-term population viability, vital rate correlations may importantly affect p...
14 CitationsSource
#1David N. Koons (USU: Utah State University)H-Index: 24
#2David T. Iles (USU: Utah State University)H-Index: 5
Last. HalCaswell (UvA: University of Amsterdam)H-Index: 67
view all 4 authors...
Current understanding of life-history evolution and how demographic parameters contribute to population dynamics across species is largely based on assumptions of either constant environments or stationary environmental variation. Meanwhile, species are faced with non-stationary environmental conditions (changing mean, variance, or both) created by climate and landscape change. To close the gap between contemporary reality and demographic theory, we develop a set of transient life table response...
23 CitationsSource
#1Jenni L. McDonald (University of Exeter)H-Index: 6
#2Trevor C. Bailey (University of Exeter)H-Index: 36
Last. Dave J. Hodgson (University of Exeter)H-Index: 16
view all 6 authors...
Demographic buffering allows populations to persist by compensating for fluctuations in vital rates, including disease‐induced mortality. Using long‐term data on a badger (Meles meles Linnaeus, 1758) population naturally infected with Mycobacterium bovis, we built an integrated population model to quantify impacts of disease, density and environmental drivers on survival and recruitment. Badgers exhibit a slow life‐history strategy, having high rates of adult survival with low variance, and low ...
16 CitationsSource
#1Roberto Salguero-Gómez (MPG: Max Planck Society)H-Index: 20
#2Owen R. Jones (University of Southern Denmark)H-Index: 22
Last. James W. Vaupel (MPG: Max Planck Society)H-Index: 79
view all 22 authors...
1. The open-data scientific philosophy is being widely adopted and proving to promote considerable progress in ecology and evolution. Open-data global data bases now exist on animal migration, species distribution, conservation status, etc. However, a gap exists for data on population dynamics spanning the rich diversity of the animal kingdom world-wide. This information is fundamental to our understanding of the conditions that have shaped variation in animal life histories and their relationsh...
62 CitationsSource
#1Sascha van der Meer (Katholieke Universiteit Leuven)H-Index: 4
#2Hans Jacquemyn (Katholieke Universiteit Leuven)H-Index: 37
Last. Eelke Jongejans (Radboud University Nijmegen)H-Index: 30
view all 4 authors...
The population dynamics and distribution limits of plant species are predicted to change as the climate changes. However, it remains unclear to what extent climate variables affect population dynamics, which vital rates are most sensitive to climate change, and whether the same vital rates drive population dynamics in different populations. In this study, we used long-term demographic data from two populations of the terrestrial orchid Himantoglossum hircinum growing at the northern edge of thei...
7 CitationsSource
#1Roberto Salguero-Gómez (MPG: Max Planck Society)H-Index: 20
#2Owen R. Jones (University of Southern Denmark)H-Index: 22
Last. James W. Vaupel (MPG: Max Planck Society)H-Index: 79
view all 37 authors...
Summary 1. Schedules of survival, growth and reproduction are key life-history traits. Data on how these traits vary among species and populations are fundamental to our understanding of the ecological conditions that have shaped plant evolution. Because these demographic schedules determine population
114 CitationsSource
#1Lam Si Tung Ho (UW: University of Wisconsin-Madison)H-Index: 6
#2Cécile Ané (UW: University of Wisconsin-Madison)H-Index: 28
We developed a linear-time algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylogenetic covariance matrix V and quadratic products involving the inverse of V. Applications include Gaussian models such as Brownian motion-derived models like Pagel's lambda, kappa, delta, and the ear...
260 CitationsSource
#1Sascha van der Meer (Katholieke Universiteit Leuven)H-Index: 4
#2Johan P. Dahlgren (Stockholm University)H-Index: 15
Last. Johan Ehrlén (Stockholm University)H-Index: 45
view all 4 authors...
Abandonment of traditional land-use practices can have strong effects on the abundance of species occurring in agricultural landscapes. However, the precise mechanisms by which individual performance and population dynamics are affected are still poorly understood. To assess how abandonment affects population dynamics of Succisa pratensis we used data from a 4-year field study in both abandoned and traditionally grazed areas in moist and mesic habitats to parameterize integral projection models....
13 CitationsSource
#1Jesús Villellas (CSIC: Spanish National Research Council)H-Index: 6
#2William F. Morris (Duke University)H-Index: 43
Last. María B. García (CSIC: Spanish National Research Council)H-Index: 28
view all 3 authors...
22 CitationsSource
Cited By0
Newest
#1Judy P. Che-Castaldo (Lincoln Park Zoo)H-Index: 7
#2Owen R. Jones (University of Southern Denmark)H-Index: 22
Last. Roberto Salguero-Gómez (University of Oxford)H-Index: 20
view all 16 authors...
Source