Transient population dynamics: Relations to life history and initial population state

Koons, David N.; Grand, James B.; Zinner, Bertram; Rockwell, Robert F.

doi:10.1016/j.ecolmodel.2004.12.011

Cited by 131 publications

(171 citation statements)

References 57 publications

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“…A particularly high growth rate could have taken place during 1996-1999 because of an unbalanced population structure, including a high proportion of adult females (47% in 1996) and a low number of individuals, thus facilitating a rapid increase in the population. Such deviations from the asymptotic age structure are widespread in vertebrate populations and, although they are generally neglected, have to be accounted for (Koons et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Higher than expected growth rate of the Endangered West African giraffeGiraffa camelopardalis peralta: a successful human–wildlife cohabitation

Suraud

Fennessy²,

Bonnaud

et al. 2012

Oryx

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The West African giraffe is a genetically unique population represented only by the subspecies Giraffa camelopardalis peralta, categorized as Endangered on the IUCN Red List. These giraffes live outside protected areas, without natural predators and share their habitat with local people and their livestock. This study provides demographic data on this poorly studied megaherbivore and documents its recovery. We analysed the results of photo-identification censuses from 1996 to 1999 (count data) and from 2005 to 2008 (count and demographic data). From 1996 to 1999 the annual growth rate was c. 19% because of an unbalanced population structure after a period of severe poaching. From 2005 to 2008 an annual growth rate of c. 12-13% was estimated from both count data and demographic parameters. This value fits with the maximum growth rate calculated for a browser species based on the allometric relationship linking growth rate and body mass. During the period 2005-2008 adult and subadult females had a constant survival rate of 0.94 and a constant recapture rate of 0.97. Annual calf survival rate was 1. Observed sex ratio at birth was 0.57 and mean reproductive success was 0.257. Generation time was estimated to be 9.66 years. This spectacular population growth was mostly attributed to the absence of predators and the ongoing monitoring to limit illegal hunting.

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Section: Discussionmentioning

confidence: 99%

Higher than expected growth rate of the Endangered West African giraffeGiraffa camelopardalis peralta: a successful human–wildlife cohabitation

Suraud

Fennessy²,

Bonnaud

et al. 2012

Oryx

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“…Deviations away from the stable stage distribution change the population dynamics, resulting in sometimes dramatically different transient dynamics (Townley et al 2007). The scale of the deviation and the time until the population returns to the stable stage distribution depend upon the population matrix structure, parameter values, and hence the net reproductive value of a population (Koons et al 2005). If matrix size influences predicted transient dynamics it raises the question of how many life-history stages need to be included in a matrix to accurately predict the nature of transient population dynamics of real populations.…”

Section: Introductionmentioning

confidence: 99%

“…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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Transient population dynamics: Relations to life history and initial population state

Cited by 131 publications

References 57 publications

Higher than expected growth rate of the Endangered West African giraffeGiraffa camelopardalis peralta: a successful human–wildlife cohabitation

Higher than expected growth rate of the Endangered West African giraffeGiraffa camelopardalis peralta: a successful human–wildlife cohabitation

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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