Catch Per Unit Effort for Moose: A New Approach Using Weibull Regression

Schmidt, Jennifer I.; Hoef, Jay M. Ver; Maier, Julie A. K.; Bowyer, R. Terry

doi:10.2193/0022-541x(2005)069[1112:cpuefm]2.0.co;2

Cited by 33 publications

(56 citation statements)

References 40 publications

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“…Unfortunately, we were unable to calculate the catch per unit effort (CPUE), which is another useful index of deer abundance (e.g., Crête et al 1981, Fryxell et al 1988, Hatter 2001, Schmidt et al 2005. This is because harvest data, until recently, only has been reported as the municipality sum of moose killed, and because the moose observation form was not submitted by all hunting teams.…”

Section: Harvest and Hunter Moose Observation Datamentioning

confidence: 99%

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: Harvest and Hunter Moose Observation Datamentioning

confidence: 99%

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

et al. 2014

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“…For example, OHVs have been used as the primary means of transportation for subsistence hunting and gathering of wild food resources in many rural communities (Berkes and Jolly 2001, Ford et al 2006, Brinkman et al 2007). OHV users have used existing infrastructure, such as old logging roads, in addition to creating an extensive informal network of dispersed routes in the process of searching for game (Mills and Firman 1986, Stedman et al 2004, Schmidt et al 2005.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the road-effect zone on wildlife distribution in a rural landscape

Shanley

Pyare

2011

Ecosphere

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Abstract. The road-effect zone is the area in which ecological effects extend outward from a road.Dispersed off-highway vehicle (OHV; e.g., four-wheelers and snowmachines) activity on rural road networks creates a disturbance that reduces the effective amount of wildlife habitat and therefore has the potential for an extensive road-effect zone. Consequently, land managers must consider the trade-offs between rural road development and the conservation of habitat for species of concern. We conducted a spatially-explicit study of moose, Alces alces, occurrence in relation to rural roads and OHV routes in rural Alaska, U.S.A. We used logistic regression and AIC model selection criterion to develop resource selection functions (RSFs) for male and female moose at three spatial scales (250 m, 500 m, and 1000 m) in two seasons (summer and fall). To evaluate an ecological disturbance threshold from increasing route activity on the probability of animal occurrence, the RSFs were plotted against an index of route activity derived from interviews with OHV users, and fit with logarithmic functions. The variable for route activity improved the fit of RSF models for both sexes at all spatial scales and in both seasons. A negative relationship was found between moose occurrence and routes or areas in which routes were in close proximity to primary forage, with the exception of male moose at the 1000-m scale in the fall. Therefore, among the spatial scales of analysis, the road-effect zone for male moose was determined to be between 500 m and 1000 m, and .1000 m for female moose. Furthermore, route activity ,0.25 km of vehicle travel/km 2 / day was a threshold value at which moose sustained a high probability of occurrence (0.60 to 0.91). The results of our study suggest that the dispersed ecological effect of rural roads and OHV routes should be considered in transportation and land-management planning efforts. Relatively low levels of vehicular activity may create extensive road-effect zones for sensitive species.

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“…Regarding the latter, managers may consider adding more hunters or requiring more effort from individual hunters, each solution having its own limitations. At some point, too many hunters may serve to educate deer to predation risk or may result in greater interference among hunters (Schmidt et al, 2005) both of which may unintentionally erode harvests. On the other hand, asking more effort of volunteer sportsman is limited by how much time hunters will choose to or be able to contribute in the face of diminishing returns on their efforts (Van Deelen and Etter, 2003).…”

Section: Discussionmentioning

confidence: 99%