Ecology: On the regulation of populations of mammals, birds, fish, and insects

University of Canberra, Canberra, Australian Capital Territory, Australia
Science (Impact Factor: 33.61). 08/2005; 309(5734):607-10. DOI: 10.1126/science.1110760
Source: PubMed


A key unresolved question in population ecology concerns the relationship between a population's size and its growth rate.
We estimated this relationship for 1780 time series of mammals, birds, fish, and insects. We found that rates of population
growth are high at low population densities but, contrary to previous predictions, decline rapidly with increasing population
size and then flatten out, for all four taxa. This produces a strongly concave relationship between a population's growth
rate and its size. These findings have fundamental implications for our understanding of animals' lives, suggesting in particular
that many animals in these taxa will be found living at densities above the carrying capacity of their environments.

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Available from: Jim Hone, Oct 10, 2015
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    • "We base our analysis of HM bison population dynamics on the discrete-time Gompertz model N t ¼ N tÀ1 exp r þ b 3 logðN tÀ1 Þ ð1Þ where r is the intrinsic rate of growth from low abundance (N ¼ 1), t is time, and b is the strength of density dependence (Turchin 2003, Dennis et al. 2006). Given its tendency to better describe compensatory density dependence (Sibly et al. 2005), we chose the Gompertz model for bison that exhibit delayed age at maturity and a maximum of one calf per year among mature females (Meagher 1986) rather than, e.g., the Ricker model that might be preferred for a more fecund species that could experience overcompensatory dynamics (Turchin 2003). On the logarithmic scale, the Gompertz model becomes linear and easier to work with logðN t Þ ¼ x t ¼ x tÀ1 þ r þ bx tÀ1 : "
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    ABSTRACT: Understanding the relative effects of climate, harvest, and density dependence on population dynamics is critical for guiding sound population management, especially for ungulates in arid and semiarid environments experiencing climate change. To address these issues for bison in southern Utah, USA, we applied a Bayesian state-space model to a 72-yr time series of abundance counts. While accounting for known harvest (as well as live removal) from the population, we found that the bison population in southern Utah exhibited a strong potential to grow from low density (β0 = 0.26; Bayesian credible interval based on 95% of the highest posterior density [BCI] = 0.19-0.33), and weak but statistically significant density dependence (β1 = -0.02, BCI = -0.04 to -0.004). Early spring temperatures also had strong positive effects on population growth (βfat1 = 0.09, BCI = 0.04-0.14), much more so than precipitation and other temperature-related variables (model weight > three times more than that for other climate variables). Although we hypothesized that harvest is the primary driving force of bison population dynamics in southern Utah, our elasticity analysis indicated that changes in early spring temperature could have a greater relative effect on equilibrium abundance than either harvest or the strength of density dependence. Our findings highlight the utility of incorporating elasticity analyses into state-space population models, and the need to include climatic processes in wildlife management policies and planning.
    Ecological Applications 06/2015; 25(4):956-967. DOI:10.1890/14-0932.1 · 4.09 Impact Factor
    • "eter θ =1 , the theta - Ricker reduces to the more parsimonious Ricker model . Although parameter θ is important for characterizing variabil - ity in density dependence , its shortcoming is that it is sensitive to patterns in population growth and variability that emerge during the curve - fitting process ( Clark and Brook , 2010 ) . For example , Sibly et al . ( 2005 ) fit the theta - Logistic model ( i . e . , continuous - time version of the theta - Ricker model ) to datasets from the Global Population Dynamics Database ( GPDD ) and found that only 20 . 6% ( n = 3269 ) of the total sample of popu - lation time series could be reliably fit using this approach ( Getz and Lloyd - Smith , 2006 ; Ross "
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    ABSTRACT: Population density regulation is a fundamental principle in ecology, but the specific process underlying functional expression of density dependence remains to be fully elucidated. One view contends that patterns of density dependence are largely fixed across a species irrespective of environmental conditions, whereas another is that the strength and expression of density dependence are fundamentally variable depending on the nature of exogenous or endogenous constraints acting on the population. We conducted a study investigating the expression of density dependence in Chlamydomonas spp. grown under a gradient from low to high nutrient density. We predicted that the relationship between per capita growth rate (pgr) and population density would vary from concave up to concave down as nutrient density became less limiting and populations experienced weaker density regulation. Contrary to prediction, we found that the relationship between pgr and density became increasingly concave-up as nutrient levels increased. We also found that variation in pgr increased, and pgr levels reached higher maxima in nutrient-limited environments. Most likely, these results are attributable to population growth suppression in environments with high intraspecific competition due to limited nutrient resources. Our results suggest that density regulation is strongly variable depending on exogenous and endogenous processes acting on the population, implying that expression of density dependence depends extensively on local conditions. Additional experimental work should reveal the mechanisms influencing how the expression of density dependence varies across populations through space and time.
    04/2015; 3. DOI:10.3389/fevo.2015.00037
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    • "This method paves the way for a deterministic evaluation of the extinction risk of rare and sparse populations, detecting demographic thresholds at which a population is endangered or threatened. Indeed, at an advanced level, our density-based approach structured on the different life stages of a population can be used to make predictions and optimize the management of critical areas where conservation has to be preserved (Sibly et al., 2005; Garrett and Bowden, 2002; Davis et al., 2004; McCormick et al., 2010; Beissinger and McCullough, 2002; Caughley and Sinclair, 1994; Pastorok et al., 2002 "
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    ABSTRACT: Depensation in a population growth rate, well-known as Allee effect, has dramatic implications for the dynamics and conservation of small or sparse populations, as it can drive low-density populations to extinction when their demographic size is below a critical threshold. Although rarely detected, depensation effects are believed to be common in nature. Here we present experimental evidence for Allee effect in one Mediterranean endemic plant: Anchusa sardoa. Depensation in the population growth rate is demonstrated through a density-based approach by showing the fingerprinting relationship which ties the population density to its per capita growth rate (pgr) during specific stages of the plant life-cycle. The pgr–density plots derived from observational data qualitatively compare with a general 2nd order polynomial function which features one of the peculiar trends underlying an Allee mechanism. We found strong evidence for depensation in the seedling and sapling classes, whereas no-depensation effect could be clearly observed in the adult classes. We also point out a characteristic demographic structure of A. sardoa (i.e. number of juveniles > number of adults) which reflects a not common life strategy with respect to Mediterranean endemic plants. By combining dynamical and demographic information, the results of this study suggest a possible scenario by which A. sardoa population could go extinct, and are discussed in the context of the increasing mass tourism in Mediterranean coastal environments.
    Ecological Complexity 12/2014; 20. DOI:10.1016/j.ecocom.2014.09.007 · 1.93 Impact Factor
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