In Sweden, reporting is voluntary for most common felled game, and the number of voluntary reports can vary between hunting teams, HMP, and counties. In 2020, an improved harvest estimation model was developed, which reduced the sensitivity to low reporting. However, there were still some limits to the model, where large, credible intervals were estimated. Therefore, additional variables were considered as the model does not take into account landcover among HMPs, [2] the impact of climate, [4] wildlife accidents, and [4] geographical distribution, creating the covariate model. This study aimed to compare the new model with the covariate model to see if covariates would reduce the large, credible intervals. Two hypothesis tests were performed: evaluation of predictive performance using leave one out cross-validation and evaluation of the 95 % credible interval. Evaluation of predictive performance was performed by examining the difference in expected log-pointwise predictive density (ELPD) and standard error (SE) for each species and model. The results show that the covariates model ranked highest for all ten species, and out of the ten species, six had an (ELPD) difference of two to four, which implies that there is support that the covariate model will be a better predictor for other datasets than this one. At least one covariate had an apparent effect on harvest estimates for nine out of ten species. Finally, the covariate model reduced the large uncertainties, which was an improvement of the null model, indicating that harvest estimates can be improved by taking covariates into account.
Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-178002 |
Date | January 2021 |
Creators | Jonsson, Paula |
Publisher | Linköpings universitet, Institutionen för fysik, kemi och biologi |
Source Sets | DiVA Archive at Upsalla University |
Language | English |
Detected Language | English |
Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
Format | application/pdf |
Rights | info:eu-repo/semantics/openAccess |
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