Evaluation of four methods used to estimate population density of moose Alces alces

Rönnegård, Lars; Sand, Håkan; Andrén, Henrik; Månsson, Johan; Pehrson, Åke

doi:10.2981/0909-6396(2008)14[358:eofmut]2.0.co;2

Cited by 66 publications

(77 citation statements)

References 29 publications

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“…As for comparison between areas, HD was a fairly good reflector of the variation in moose density, while SPUE and SMD failed to represent the spatial variation in moose density. However, both observation indices provided a good representation of the temporal variation in moose density within study sites, consistent with previous studies (Ericsson and Wallin 1999, Rö nnegå rd et al 2008. Part of the spatial variation in SPUE was explained by the proportion of deciduous forests, which we believe can have a strong effect on the visual transparency of the forest and thus the probability of detecting a moose.…”

Section: Discussionsupporting

confidence: 89%

“…However, as the population abundance is seldom known, an alternative is to compare them with other independent indices or estimates of density (e.g., Skalski et al 2007). For instance, by examining the relationship between the moose, Alces alces, seen per unit effort (SPUE) and moose abundance reconstructed by cohort analysis, concluded that the SPUE may be used for monitoring the temporal variation in moose abundance (see also Rö nnegå rd et al 2008). For many indices, however, the relationship to abundance is at best based on a few studies and comparative merits of indices are unclear.…”

Section: Introductionmentioning

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: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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“…To get an idea of how well harvest density relates to moose density, and with what time lag, we used a subset of years (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) for which we also possessed data on moose seen per hunter-day. This index is based on a large number of moose observations recorded by moose hunters each year and is found to correlate closely with moose density (Ericsson and Wallin 1999, Sylven 2000, Solberg et al 2006, Ronnegard et al 2008. The correlation between moose seen per hunter-day and harvest density in the same year was on average positive (mean r ¼ 0.52, range: À0.61-0.90), indicating that harvest density was quite well tracking the variation in moose density within county.…”

Section: Moose Densitymentioning

confidence: 97%

Large-scale spatiotemporal variation in road mortality of moose: Is it all about population density?

et al. 2011

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Ungulate‐vehicle collisions (UVC) constitute a widespread and increasing problem in large parts of the world. This has generated an intensive search for mitigating measures, but often based on a weak understanding of the underlying spatiotemporal factors. We examined the effects of harvest density (a proxy for moose density), traffic‐related variables and climate on the spatiotemporal variation in number of moose‐vehicle collisions (MVC) in 14 Norwegian counties based on 31 year of data. Moose density was the most important factor explaining the variation in MVC, both within and between counties. In addition, the spatiotemporal variation in MVC was positively related to traffic volume (private car mileage) and snow depth, and negatively related to winter temperature. The relationship between traffic volume and temporal variation in MVC was stronger in counties with general low traffic volume, possibly because high traffic volume can act as a barrier to moose road crossings. Likewise, the temporal effect of snow depth was mainly present in counties with on average deep snow, i.e., where it constitutes a constraint on moose movement and space use during winter. Our study highlights the different importance between areas of the factors underlying the spatiotemporal variation in MVC. A notable exception was the variation in moose density, which follows an isometric scaling to the variation in MVC in all counties. Thus, a given percentage decrease in moose density is likely to return a similar percentage decrease in MVC. A significant population reduction may therefore be an efficient mitigating measure to reduce the number of MVC in Norway. From a harvesting and conservation point of view, other possible preventive measures to reduce MVC should also be considered. However, because of the strong temporal effects of moose density and snow depth, evaluations of other mitigating actions should always seek to control for temporal variation in these variables.

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“…Månsson et al (2011) compared three survey methods in a small area and estimated that the total costs of the aerial count was about 27,000 Euros, for the pellet counts it cost 8400 Euros, and for hunters' observations it cost 1600 Euros. Similarly, Rönnegård et al (2008) evaluated four methods: aerial counts, hunters' observations, pellet group counts, and cohort analysis, and they showed that the hunter's observations could be used to estimate long term trends even in moderately sized areas (~500 km²).…”

Section: Scientific Credibility and Cost Effectivenessmentioning

confidence: 99%

Tackling the motivation to monitor: success and sustainability of a participatory monitoring program

Singh¹,

Danell²,

Edenius³

et al. 2014

E&S

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ABSTRACT. Monitoring of species and their ecosystem attributes is a fundamental requirement in applied ecology and conservation. However, landscape scale monitoring requires an immense effort and commitment, especially when species have a wide distribution or are migratory in nature. Participatory monitoring, whereby local communities are engaged, is increasingly being proposed to address landscape scale monitoring. Its implementation is met with many challenges related to finances, motivation of the local people, lack of trained manpower, and nondirect legal use of the species in question. It is of interest to determine what makes a participatory monitoring program interesting for locals to ensure their long term engagement. Using the unique 26-year program of hunters' observations of moose (Alces alces) in Sweden as a case study, we present the evolution of this highly successful participatory monitoring program and show that tackling the motivation to monitor, early involvement of local NGOs, social activities revolving around use of the resource, the biology and economic value of the species, and technical and practical aspects related to the monitoring, together create a successful participatory monitoring program. When users benefit directly from the resource, participate in conservation/ management decision making, socialize with other participants, and get rewards for their commitment and effective monitoring, participatory monitoring schemes can then become rewarding and sustainable.

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Evaluation of four methods used to estimate population density of moose Alces alces

Cited by 66 publications

References 29 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

Large-scale spatiotemporal variation in road mortality of moose: Is it all about population density?

Tackling the motivation to monitor: success and sustainability of a participatory monitoring program

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