A Review of Density Dependence in Populations of Large Mammals

Fowler, Charles W.

doi:10.1007/978-1-4757-9909-5_10

Cited by 356 publications

(299 citation statements)

References 102 publications

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“…Various nonlinear functions can yield qualitatively similar results. In fact, in many populations density dependence is strongest as the population approaches K (Fowler 1987), as seen, for example, in wildebeest, Connoclzaetes taurilzus (Mduma et al 1999), which can amplify compensation (Fig. 7).…”

Section: V1 Seasonally Explicit Discrete-time Survivalmentioning

confidence: 99%

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Seasonal Compensation of Predation and Harvesting

Boyce¹,

Sinclair²,

White³

1999

Oikos

226

216

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Section: V1 Seasonally Explicit Discrete-time Survivalmentioning

confidence: 99%

“…Examples include beavers (Castor canadensis; Boyce 1981) and muskrats (Clark 1987). Fowler (1987) has compiled a catalogue of density-dependent responses by mammals.…”

Section: Biological Mechanismsmentioning

confidence: 99%

Seasonal Compensation of Predation and Harvesting

Boyce¹,

Sinclair²,

White³

1999

Oikos

226

216

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“…In the absence of empirical data for polar bears (Taylor, 1994), we used insights from population and evolutionary ecology to develop the functions. For large mammals, density-dependent effects typically appear first in subadult survival rates, then in breeding rates and juvenile survival, and finally in adult survival (Fowler, 1987). The relative positions of the inflection points of the curves for each vital rate were determined by the order in which polar bear life-history events are affected by density (e.g., σ L0 typically decreases before β 4 ).…”

Section: Relationships Between Vital Rates and Densitymentioning

confidence: 99%

Resilience and risk: a demographic model to inform conservation planning for polar bears

Regehr¹,

Wilson²,

Rode³

et al. 2015

Open-File Report

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For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment-visit http://www.usgs.gov or call 1-888-ASK-USGS (1-888-275-8747) For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprodTo order this and other USGS information products, visit http://store.usgs.gov Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.Suggested citation: Regehr, E.V., Wilson, R.R., Rode, K.D., and Runge, M.C., 2015, Resilience and risk-A demographic model to inform conservation planning for polar bears: U.S. Geological Survey Open-File Report 2015-1029, 56 p., http://dx.doi.org/10.3133/ofr20151029. ISSN 2331ISSN -1258 iii Table 5. Range of survival rates that must be maintained, in conjunction with sufficient recruitment, to achieve a 90-percent probability of persistence over 100 years . Executive SummaryClimate change is having widespread ecological effects, including loss of Arctic sea ice. This has led to listing of the polar bear (Ursus maritimus) and other ice-dependent marine mammals under the U.S. Endangered Species Act. Methods are needed to evaluate the effects of climate change on population persistence to inform recovery planning for listed species. For polar bears, this includes understanding interactions between climate and secondary factors, such as subsistence harvest, which provide economic, nutritional, or cultural value to humans.We developed a matrix-based demographic model for polar bears that can be used for population viability analysis and to evaluate the effects of human-caused removals. This model includes densitydependence (the potential for a declining environmental carrying capacity), density-independent limitation, and sex-and age-specific harvest vulnerabilities. We estimated values of adult female survival (0.93-0.96), recruitment (number of yearling cubs per adult female; 0.1-0.3), and carrying capacity (>250 animals) that must be maintained for a hypothetical population to achieve a 90-percent probability of persistence over 100 years.We also developed a state-dependent management framework, based on harvest theory and the potential biological removal method, by linking the demographic model to simulated population assessments. This framework can be used to estimate the maximum sustainable rate of human-caused removals, including subsistence harvest, which maintains a population at its maximum net productivity level. The framework also can be used to calculate a recommended sustainable harvest rate, which generally is lower than the maximum sustainable rate and depends on management objectives, the precision and frequency of population data, ...

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“…No annual variation in M and no age-specific variation in M among adult females were assumed. Because survival rate for yearlings and adults is generally high and stable, the effect of these assumptions may be of minor importance in large mammals (Fowler 1987;Saether 1997;Gaillard et al 1998Gaillard et al , 2000. However, because senescence has been detected after 7-9 years of age in ungulate populations (Gaillard et al 1993, Festa-Bianchet et al 2003, higher natural mortalities for older individuals may make the reconstructed population more realistic.…”

Section: Natural Mortality Rate (M)mentioning

confidence: 99%

Application of Cohort Analysis to Large Terrestrial Mammal Harvest Data

et al. 2009

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Abstract. Cohort analysis (also known as virtual population analysis) is a method of population reconstruction from age-specific harvest data. Because cohort analysis requires data over a whole life span to reconstruct a population for a single year, this method is impracticable for longer-lived animals. Three models are routinely combined by fisheries scientists to make cohort analysis more cost effective and to provide real-time estimates of population size; these models may be applied to large terrestrial mammal harvest data. Each model has unique assumptions about hunting mortality rates or age distributions, and the reliability of estimates depends on meeting these assumptions. In this study, we first tested previously used assumptions for these models through an analysis of longterm moose (Alces alces) harvest data, followed by an examination of the robustness of estimates for each moose age class. We developed practical ways to achieve more realistic assumptions for two of three models and showed that meeting these assumptions was more important in estimations of large terrestrial mammal population parameters than for fish population parameters. Therefore, we recommend compliance with assumptions of the three models for more reliable population estimates of large terrestrial mammals.

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A Review of Density Dependence in Populations of Large Mammals

Cited by 356 publications

References 102 publications

Seasonal Compensation of Predation and Harvesting

Seasonal Compensation of Predation and Harvesting

Resilience and risk: a demographic model to inform conservation planning for polar bears

Application of Cohort Analysis to Large Terrestrial Mammal Harvest Data

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