TY - JOUR
T1 - The effect of demographic correlations on the stochastic population dynamics of perennial plants
AU - Compagnoni, Aldo
AU - Bibian, Andrew J.
AU - Ochocki, Brad M.
AU - Rogers, Haldre S.
AU - Schultz, Emily L.
AU - Sneck, Michelle E.
AU - Elderd, Bret D.
AU - Iler, Amy M.
AU - Inouye, David W.
AU - Jacquemyn, Hans
AU - Miller, Tom E.X.
N1 - Funding Information:
We thank Margaret E. K. Evans, Ines Ib??ez, Lorenzo Boninsegna, and Duncan Wadsworth for useful discussions. We thank two anonymous reviewers for improving the quality of this manuscript. This study was funded by grants from the National Science Foundation to T. E. X. Miller (DEB-1145588, DEB-1543651), B. D. Elderd (DEB-1354104), and D. W. Inouye (IBN-98-14509, DEB-0238331, DEB-0922080). We thank the many students who have contributed to our demographic studies. Op. imbricata demographic data were collected with support from the Sevilleta National Wildlife Refuge and Sevilleta LTER (NSF DEB-0620482 and DEB-1232294).
PY - 2016/11/1
Y1 - 2016/11/1
N2 - 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 population dynamics in stochastic environments. Despite long-standing theoretical interest, it is unclear whether vital rate correlations are common in nature, whether their directions are predominantly negative or positive, and whether they are of sufficient magnitude to warrant broad consideration in studies of stochastic population dynamics. We used long-term demographic data for three perennial plant species, hierarchical Bayesian parameterization of population projection models, and stochastic simulations to address the following questions: (1) What are the sign, magnitude, and uncertainty of temporal correlations between vital rates? (2) How do specific pairwise correlations affect the year-to-year variability of population growth? (3) Does the net effect of all vital rate correlations increase or decrease year-to-year variability? (4) What is the net effect of vital rate correlations on the long-term stochastic population growth rate (λs)? We found only four moderate to strong correlations, both positive and negative in sign, across all species and vital rate pairs; otherwise, correlations were generally weak in magnitude and variable in sign. The net effect of vital rate correlations ranged from a slight decrease to an increase in the year-to-year variability of population growth, with average changes in variance ranging from -1% to +22%. However, vital rate correlations caused virtually no change in the estimates of λs (mean effects ranging from -0.01% to +0.17%). Therefore, the proportional changes in the variance of population growth caused by demographic correlations were too small on an absolute scale to importantly affect population growth and viability. We conclude that, in our three focal populations and perhaps more generally, vital rate correlations have little effect on stochastic population dynamics. This may be good news for population ecologists, because estimating vital rate correlations and incorporating them into population models can be data intensive and technically challenging.
AB - 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 population dynamics in stochastic environments. Despite long-standing theoretical interest, it is unclear whether vital rate correlations are common in nature, whether their directions are predominantly negative or positive, and whether they are of sufficient magnitude to warrant broad consideration in studies of stochastic population dynamics. We used long-term demographic data for three perennial plant species, hierarchical Bayesian parameterization of population projection models, and stochastic simulations to address the following questions: (1) What are the sign, magnitude, and uncertainty of temporal correlations between vital rates? (2) How do specific pairwise correlations affect the year-to-year variability of population growth? (3) Does the net effect of all vital rate correlations increase or decrease year-to-year variability? (4) What is the net effect of vital rate correlations on the long-term stochastic population growth rate (λs)? We found only four moderate to strong correlations, both positive and negative in sign, across all species and vital rate pairs; otherwise, correlations were generally weak in magnitude and variable in sign. The net effect of vital rate correlations ranged from a slight decrease to an increase in the year-to-year variability of population growth, with average changes in variance ranging from -1% to +22%. However, vital rate correlations caused virtually no change in the estimates of λs (mean effects ranging from -0.01% to +0.17%). Therefore, the proportional changes in the variance of population growth caused by demographic correlations were too small on an absolute scale to importantly affect population growth and viability. We conclude that, in our three focal populations and perhaps more generally, vital rate correlations have little effect on stochastic population dynamics. This may be good news for population ecologists, because estimating vital rate correlations and incorporating them into population models can be data intensive and technically challenging.
KW - Demographic buffering
KW - Demographic correlation
KW - Environmental stochasticity
KW - Generalized linear mixed models (GLMM)
KW - Hierarchical Bayes
KW - Integral projection model (IPM)
KW - Stochastic population growth rate
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U2 - 10.1002/ecm.1228
DO - 10.1002/ecm.1228
M3 - Article
AN - SCOPUS:84991080322
VL - 86
SP - 480
EP - 494
JO - Ecological Monographs
JF - Ecological Monographs
SN - 0012-9615
IS - 4
ER -