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

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 km2), (2) moose seen per unit effort (SPUE), seen moose density (SMD; seen moose per km2), and density of moosevehicle accidents (MVA density; e.g., traffic kills per km2) 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 km2 provides a precise and isometric index of the variation in moose density at the spatial scale of our study. cohort analysis; isometric index; management; monitoring; population reconstruction; precision; saturation; seen per unit effort (SPUE).Performance of hunting statistics as spatiotemporal density indices of moose (Alces alces) in NorwaypublishedVersio

Classification of breed combinations for slaughter pigs based on genotypes—modeling DNA samples of crossbreeds as fuzzy sets from purebred founders

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Moose project Akershus - Part 1: Cameramonitoring of wildlife crossing structures and area use of moose in Øvre Romerike

Elgprosjektet i Akershus har hatt som hovedformål å kartlegge hvordan faunapassasjer fungerer. Det ble samlet inn data fra 55 GPS-merket elg og 20 utvalgte faunapassasjer ble overvåket med viltkamera. Prosjektet har bidratt med relevant kunnskap som kan være til nytte for fremtidige veg- og jernbaneprosjekter, hvor kryssingsmuligheter for vilt er tema. Denne rapporten er utgitt i forbindelse med Etatsprosjekt Vinterdrift. Det er gjennomført en spørreundersøkelse for å kartlegge hvilke krav nordiske vegmyndigheter og kommuner stiller til vinterdrift av gang- /sykkelveger og fortau.Statens vegvesen Vegdirektorate

Moose project Akershus - Summary report

Elgprosjektet i Akershus har hatt som hovedformål å kartlegge hvordan faunapassasjer fungerer. Det ble samlet inn data fra 55 GPS-merket elg og 20 utvalgte faunapassasjer ble overvåket med viltkamera. Prosjektet har bidratt med relevant kunnskap som kan være til nytte for fremtidige veg- og jernbaneprosjekter, hvor kryssingsmuligheter for vilt er tema.Statens vegvesen Vegdirektorate

Theoretical evaluation of prediction error in linear regression with a bivariate response variable containing missing data

Methods for linear regression with multivariate response variables are well described in statistical literature. In this study we conduct a theoretical evaluation of the expected squared prediction error in bivariate linear regression where one of the response variables contains missing data. We make the assumption of known covariance structure for the error terms. On this basis, we evaluate three well-known estimators: standard ordinary least squares, generalized least squares, and a James–Stein inspired estimator. Theoretical risk functions are worked out for all three estimators to evaluate under which circumstances it is advantageous to take the error covariance structure into account.acceptedVersio

Lineære multiresponsmodeller : teoretiske nyvinninger og praktiske anvendelser for svin

The main topic of this PhD–thesis is how to minimize the prediction error for multi–response linear regression models. Two different applications are analysed, (i) bivariate response with missing data and (ii) image analysis from computed tomography (ct). Both applications were initialized by practical problems in porcine.Hovedtemaet i denne PhD–avhandlingen er metodikk for å redusere prediksjonsfeil i linære regresjonsmodeller med flere responsvariabler. To ulike bruksområder, (i) bivariat respons med manglende data og (ii) 3D bildeanalyse av data fra computertomografi (ct), blir behandlet. Begge har utganspunkt i praktiske problemstillinger fra svineproduksjon

A Bayesian method for estimating moose (Alces alces) population size based on hunter observations and killed at age data

Lots of wild species, fish and mammals, are heavy harvested through fishing and hunting. Reliable population size estimates are valuable management tools for these species. In cases where killed at age data are available, models outlined under the framework known as ”cohort analysis” or ”virtual population analysis (VPA)” are used extensively. In fish stock management several models using Bayesian techniques have been developed through the last two decades. In this study a model using a Bayesian approach for estimating moose population size is examined. The model combines principles from discrete time series analysis, where basic cohort analysis based on killed at age data constitutes the bulk, and analysis in continuous time for each hunting season based on data from hunter observations. The analysis in continuous time aims to find age- and year-specific expressions for the hunting mortality rate. In the discrete time series analysis, the hunting yield is viewed as a binomially distributed variable, with pre-harvest population size as ”number of trials” and mortality rate derived from the analysis in continuous time as ”probability parameter”. All basic principles are known from previous surveys, but the way they are assembled is, to the authors knowledge, innovative. The model performed very well when tested against simulated populations with known parameter values. For real data tests are conducted through cross-validation based on spatial subsets and by comparing results from temporal subsets. Generally the model performed well in these test. However, an issue is revealed by comparing results from different temporal subsets, since the hunters ability to spot moose seems to develop over time (years) and/or depend on moose density. This issue should not terminate the practical implementation of the model. If a satisfying solution to the issue is achieved, it might have a possible positive impact on other methods for estimating abundance of wild species based on effort, a very prevalent class of models. The real data used for testing the model, and to demonstrate some practical interpretations, are from the municipality of Ringerike in southern Norway. Killed at age data are available from 1988 till 2012 in combination with hunter observations. The estimates show a moose population size rapidly increasing in the period from 1988 till its peak in 1993 at a posterior mean population size of approximately 3900 individuals. Thereafter, in line with large hunting yields, reduced reproductivity rate and increased rate of natural mortality, the population size declined rapidly till an estimated pre-harvest population size of approximately 1700 individuals in year 2000. Thereafter the total population size has been estimated as quite stable, but with a declining trend over the last couple of years. Usually the natural (non harvest) mortality rate is assumed fixed and known when cohort analysis methods are used for estimating abundance of wild species. The model presented in this study is capable of producing reliable, and to some extent practical beneficial, posterior distributions for the natural mortality rate based on an informative prior distribution and an adequate amount of data. These posterior distributions for natural mortality rates indicate surprisingly high rates for the years around 1993