Application of Cohort Analysis to Large Terrestrial Mammal Harvest Data

Ueno, Mayumi; Matsuishi, Takashi; Solberg, Erling Johan; Saitoh, Takashi

doi:10.3106/041.034.0202

Cited by 11 publications

(28 citation statements)

References 31 publications

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“…This method was originally developed within the fishery sciences (e.g., Gulland 1965, Pope 1972), but has also been extensively used to reconstruct populations of ungulates, such as moose (e.g., Fryxell et al 1988, Vucetich and Peterson 2004, Ueno et al 2009). …”

Section: Population Reconstruction By Cohort Analysismentioning

confidence: 99%

“…Conversely, the term exp(ÀNM) is the survival rate from natural causes, i.e., the survival rate becomes smaller as the coefficient gets larger. The mortality coefficient ranges from 0 to infinity, and is more mathematically convenient to use than the mortality rate (Ueno et al 2009). In this study, we assumed NM to be 0.05, with no annual variation.…”

Section: Population Reconstruction By Cohort Analysismentioning

confidence: 99%

“…The initial values are moose abundance at the oldest age group, i.e., !p years for males and !q years for females (see Table 2 for p and q) or moose abundance for the latest year, i.e., the year of 2008 (Fryxell et al 1988. We computed moose abundance in the older aged group from the harvest size of the oldest age group, the estimated hunting mortality, and the assumed natural mortality (Ueno et al 2009). We estimated hunting mortality for the oldest age group by a simple linear regression of hunting mortality at age pþ (qþ for females) against the mortality at age p À 1(q À 1 for females) for cohorts that have passed completely through the population (Ueno et al 2009).…”

Section: Population Reconstruction By Cohort Analysismentioning

confidence: 99%

“…We computed moose abundance in the older aged group from the harvest size of the oldest age group, the estimated hunting mortality, and the assumed natural mortality (Ueno et al 2009). We estimated hunting mortality for the oldest age group by a simple linear regression of hunting mortality at age pþ (qþ for females) against the mortality at age p À 1(q À 1 for females) for cohorts that have passed completely through the population (Ueno et al 2009). The number of moose aged p (q for females) was computed by assuming a stationary stable age distribution in the oldest age class for each year (Ueno et al 2009).…”

Section: Population Reconstruction By Cohort Analysismentioning

confidence: 99%

“…We estimated hunting mortality for the oldest age group by a simple linear regression of hunting mortality at age pþ (qþ for females) against the mortality at age p À 1(q À 1 for females) for cohorts that have passed completely through the population (Ueno et al 2009). The number of moose aged p (q for females) was computed by assuming a stationary stable age distribution in the oldest age class for each year (Ueno et al 2009). Likewise, we computed the age-specific moose abundance for the final year from the harvest size and the estimated hunting mortality and assumed natural mortality (Ueno et al 2009).…”

Section: Population Reconstruction By Cohort Analysismentioning

confidence: 99%

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Performance of hunting statistics as spatiotemporal density indices of moose (Alces alces) in Norway

et al. 2014

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Abstract. Wildlife managers are often asking for reliable information of population density across larger spatial scales. In this study, we examined the spatiotemporal relationships between moose density as estimated by cohort analysis and the density indices (1) harvest density (HD; hunter kills per km 2 ), (2) moose seen per unit effort (SPUE), seen moose density (SMD; seen moose per km 2 ), and density of moosevehicle accidents (MVA density; e.g., traffic kills per km 2 ) in 16 areas in Norway with 13-42 years of data. HD showed a close positive relationship with moose density both within and between regions. However, the temporal variation in HD was best explained as a delayed reflection of moose density and tended to overestimate its growth and decline. Conversely, SMD and SPUE were unable to predict the spatial variation in moose density with high precision, though both indices were relatively precise temporal reflectors of moose density. However, the SPUE tended to underestimate population growth, probably because of a decrease in searching efficiency with increasing moose density. Compared to the other indices, MVA density performed poor as an index of moose density within regions, and not at all among regions, but may, because of its independent source of data, be used to cross-check population trends suggested by other indices. Our study shows that the temporal trends in moose density can be surveyed over large areas by the use of cheap indices based on data collected by hunters and local managers, and supports the general assumption that the number of moose killed per km 2 provides a precise and isometric index of the variation in moose density at the spatial scale of our study.

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Section: Population Reconstruction By Cohort Analysismentioning

confidence: 99%

Section: Population Reconstruction By Cohort Analysismentioning

confidence: 99%

Section: Population Reconstruction By Cohort Analysismentioning

confidence: 99%

Section: Population Reconstruction By Cohort Analysismentioning

confidence: 99%

Section: Population Reconstruction By Cohort Analysismentioning

confidence: 99%

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Performance of hunting statistics as spatiotemporal density indices of moose (Alces alces) in Norway

et al. 2014

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Demographic analysis of the irruptive dynamics of an introduced sika deer population

et al. 2018

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Irruptions of large herbivores, with a rapid population increase to peak abundance, are widely observed. Occasionally, there is a population crash following such peaks in abundance, after which the population recovers to form another peak typically lower than the initial. There are mathematical models describing this full cycle of irruptive dynamics. The insight for further improvement of such mathematical models will be obtained from demographic analyses of irruptive dynamics incorporating density‐dependent and density‐independent resource limitations. Using a 35‐yr dataset on the irruptive dynamics of an introduced sika deer (Cervus nippon) population on Nakanoshima Island, Japan, we evaluated the factors and stages determining irruptive dynamics (phase 1: the initial irruption and population crash; phase 2: subsequent dynamics) through key‐factor/key‐stage analysis of the population structure, estimated using age‐at‐death data of naturally dead deer. We set two factors (i.e., deer density and snow accumulation) and three life stages (i.e., immature individuals, adult female, and adult male). The estimated population structure showed two population crashes and a density peak higher than the initial peak during population regrowth subsequent to the initial population crash. The most influential factor and stage for determining the population dynamics differed between phases 1 and 2. The contribution of deer density to the variability in population change was the largest in phase 1 (62.30%) and decreased to 24.10% in phase 2. The contribution of snow accumulation was small in both phase 1 (11.74%) and phase 2 (0.64%). The male stage had the largest contribution in phase 1 (39.23%), while the immature stage had the largest contribution in phase 2 (63.31%). The female stage had the smallest contribution in both phase 1 (24.19%) and phase 2 (17.67%). Population growth rate decreased, while carrying capacity increased, in phase 2 compared to phase 1. We suggest that the small contributions of density‐dependent and density‐independent resource limitations on population dynamics in phase 2 were related to the characteristics of alternative foods (fallen leaves and woody plants) that were newly utilized by deer in phase 2. We conclude that alternative resources potentially generate various irruptive dynamics, including unstable dynamics not expected in the classic paradigm.

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Estimating red deer population size using vantage point counts at baited sites

Dijkhuis,

Jansen,

den Ouden

et al. 2023

J Wildl Manag

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Accurate estimates of population size are central to the management and conservation of wild ungulates. Counting animals from vantage points of baited sites is a method that is traditionally used in Europe to obtain abundance estimates of wild ungulates, but its reliability in forested landscapes is contested. We assessed the accuracy of vantage point counts (VPCs) for estimating population size in red deer (Cervus elaphus) at De Hoge Veluwe National Park, a fenced, partially forested game reserve in the Netherlands. We compared estimates from duplicated VPCs for 1977–2010 with population sizes reconstructed from cohort analyses of age‐specific mortality records, including and excluding supplementation with vehicular direct counts (VDCs) and field knowledge on herd composition and herd size from game wardens. Estimates from VPCs supplemented with VDCs matched the reconstructed population size well for 2001–2010. In addition, estimates based on ≥1 VPC site/11 km2 provided representative estimates of the reconstructed population size in that period. Simultaneous VPCs at multiple baited sites can yield precise population estimates for red deer in forests.

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Application of Cohort Analysis to Large Terrestrial Mammal Harvest Data

Cited by 11 publications

References 31 publications

Performance of hunting statistics as spatiotemporal density indices of moose (Alces alces) in Norway

Performance of hunting statistics as spatiotemporal density indices of moose (Alces alces) in Norway

Demographic analysis of the irruptive dynamics of an introduced sika deer population

Estimating red deer population size using vantage point counts at baited sites

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