Predicting transient amplification in perturbed ecological systems

Townley, Stuart; Carslake, David; Kellie-Smith, Owen; McCarthy, Dominic; Hodgson, David J.

doi:10.1111/j.1365-2664.2007.01333.x

Cited by 56 publications

(94 citation statements)

References 44 publications

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“…Interestingly, we also found that attenuation was more strongly predictive of the correlation between r TD and r obs than amplification. Theoretical developments on transient dynamics have been focused almost exclusively on deterministic responses to a single disturbance event (e.g., Neubert and Caswell 1997, Caswell 2007, Townley et al 2007, Townley and Hodgson 2008, Stott et al 2010. How variability in life history patterns contributes to mean attenuation and amplification potential and the differences in these two processes are not well understood.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, there has been an increasing focus on analyzing the short-term, transient dynamics that result when populations are not at SSD (e.g., Fox and Gurevitch 2000, Caswell 2007, Stott et al 2011. To date, these methods have focused on the effect of a single initial disturbance, or departure from SSD, on a constant, deterministic matrix (e.g., Neubert and Caswell 1997, Caswell 2007, Townley et al 2007, Townley and Hodgson 2008, Stott et al 2010. Because these methods consider only deterministic environments, the approach is somewhat inconsistent with much of the literature emphasizing the role of stochasticity in matrix population models.…”

Section: Introductionmentioning

confidence: 99%

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The role of transient dynamics in stochastic population growth for nine perennial plants

Ellis

Crone

2013

Ecology

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Abstract. Most populations exist in variable environments. Two sets of theory have been developed to address this variability. Stochastic dynamics focus on variation in population growth rates based on random differences in vital rates such as growth, survival, and reproduction. Transient dynamics focus on short-term, deterministic responses to changes in the stage distribution of individuals. These processes are related: demographic variation shifts stage structures, producing transient responses, which then contribute to the overall variability of population growth rate. The relative contributions of vital rates vs. transient responses to stochastic dynamics, and the implications for transient analyses, are unclear. This study explores the role of transient responses in stochastic dynamics of nine perennial plant species. Across the species, transient responses contributed more on average to variability in annual population growth rates than did variation in vital rates alone. Transient potential of an average matrix was indicative of the contribution of transient dynamics, although these metrics varied greatly across years. Transient responses were often in the opposite direction as demographic variation, suggesting that transient dynamics may at times have a buffering effect on populations. Overall, transient dynamics had an important role in modulating environmental variation, with implications for both processes in understanding stochastic dynamics.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The role of transient dynamics in stochastic population growth for nine perennial plants

Ellis

Crone

2013

Ecology

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“…Most researchers focus on analyzing the long-term, asymptotic population growth rate (k max , the largest eigenvalue of the population projection matrix), the rate at which the population grows at the stable stage distribution (but see Burgman et al 1993, Koons et al 2005, Caswell 2007, Townley et al 2007). However, a population can be perturbed away from the stable stage distribution by disturbances such as environmental catastrophes, selective harvesting regimes, and management actions (e.g., animal release and translocation programs); even in established populations the assumption that the population growth follows the asymptotic growth is unwarranted in many cases (e.g., Clutton-Brock and Coulson 2002, Koons et al 2005, Townley et al 2007). Deviations away from the stable stage distribution change the population dynamics, resulting in sometimes dramatically different transient dynamics (Townley et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Model complexity affects transient population dynamics following a dispersal event: a case study with pea aphids

Tenhumberg

Tyre

Rebarber

2009

Ecology

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Abstract. Stage-structured population models predict transient population dynamics if the population deviates from the stable stage distribution. Ecologists' interest in transient dynamics is growing because populations regularly deviate from the stable stage distribution, which can lead to transient dynamics that differ significantly from the stable stage dynamics. Because the structure of a population matrix (i.e., the number of life-history stages) can influence the predicted scale of the deviation, we explored the effect of matrix size on predicted transient dynamics and the resulting amplification of population size. First, we experimentally measured the transition rates between the different life-history stages and the adult fecundity and survival of the aphid, Acythosiphon pisum. Second, we used these data to parameterize models with different numbers of stages. Third, we compared model predictions with empirically measured transient population growth following the introduction of a single adult aphid. We find that the models with the largest number of life-history stages predicted the largest transient population growth rates, but in all models there was a considerable discrepancy between predicted and empirically measured transient peaks and a dramatic underestimation of final population sizes. For instance, the mean population size after 20 days was 2394 aphids compared to the highest predicted population size of 531 aphids; the predicted asymptotic growth rate (k max ) was consistent with the experiments. Possible explanations for this discrepancy are discussed.

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“…There is growing interest in transient population dynamics in applied ecology and invasion biology (Koons et al 2005, Townley et al 2007, McMahon and Metcalf 2008, Tenhumberg et al 2009), but few studies have considered transient spatial spread dynamics (Caswell 2007). Rather, most analyses of spatial spread focus on long-term, asymptotic predictions, when the advancing ''wave'' of organisms has reached a constant shape and a stable distribution of individuals among life stages.…”

Section: Introductionmentioning

confidence: 99%

Contributions of demography and dispersal parameters to the spatial spread of a stage‐structured insect invasion

Miller

Tenhumberg

2010

Ecological Applications

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Abstract. Stage-structured models that integrate demography and dispersal can be used to identify points in the life cycle with large effects on rates of population spatial spread, information that is vital in the development of containment strategies for invasive species. Current challenges in the application of these tools include: (1) accounting for large uncertainty in model parameters, which may violate assumptions of ''local'' perturbation metrics such as sensitivities and elasticities, and (2) forecasting not only asymptotic rates of spatial spread, as is usually done, but also transient spatial dynamics in the early stages of invasion. We developed an invasion model for the Diaprepes root weevil (DRW; Diaprepes abbreviatus [Coleoptera: Curculionidae]), a generalist herbivore that has invaded citrusgrowing regions of the United States. We synthesized data on DRW demography and dispersal and generated predictions for asymptotic and transient peak invasion speeds, accounting for parameter uncertainty. We quantified the contributions of each parameter toward invasion speed using a ''global'' perturbation analysis, and we contrasted parameter contributions during the transient and asymptotic phases. We found that the asymptotic invasion speed was 0.02-0.028 km/week, although the transient peak invasion speed (0.03-0.045 km/week) was significantly greater. Both asymptotic and transient invasions speeds were most responsive to weevil dispersal distances. However, demographic parameters that had large effects on asymptotic speed (e.g., survival of early-instar larvae) had little effect on transient speed. Comparison of the global analysis with lower-level elasticities indicated that local perturbation analysis would have generated unreliable predictions for the responsiveness of invasion speed to underlying parameters. Observed range expansion in southern Florida (1992Florida ( -2006 was significantly lower than the invasion speed predicted by the model. Possible causes of this mismatch include overestimation of dispersal distances, demographic rates, and spatiotemporal variation in parameter values. This study demonstrates that, when parameter uncertainty is large, as is often the case, global perturbation analyses are needed to identify which points in the life cycle should be targets of management. Our results also suggest that effective strategies for reducing spread during the asymptotic phase may have little effect during the transient phase.

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Predicting transient amplification in perturbed ecological systems

Cited by 56 publications

References 44 publications

The role of transient dynamics in stochastic population growth for nine perennial plants

The role of transient dynamics in stochastic population growth for nine perennial plants

Model complexity affects transient population dynamics following a dispersal event: a case study with pea aphids

Contributions of demography and dispersal parameters to the spatial spread of a stage‐structured insect invasion

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