Relative accuracy of spatial predictive models for lynx Lynx canadensis derived using logistic regression-AIC,multiple criteria evaluation and Bayesian approaches

We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems (GIS) -based approaches: logistic regression and Akaike's Information Criterion (AIC),Multiple Criteria Evaluation (MCE),and Bayesian Analysis (specifically Dempster-Shafer theory). We used lynx Lynx canadensis as our focal species,and developed our environment relationship model using track data collected in Banff National Park,Alberta,Canada,during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified (overall accuracy),the failure to predict a species where it occurred (omission error) and the prediction of presence where there was absence (commission error). Our overall accuracy showed the logistic regression approach was the most accurate (74.51% ). The multiple criteria evaluation was intermediate (39.22%),while the Dempster-Shafer (D-S) theory model was the poorest (29.90%). However,omission and commission error tell us a different story: logistic regression had the lowest commission error,while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least,the logistic regression model is optimal. However,where sample size is small or the species is very rare,it may also be useful to explore and/or use a more ecologically cautious modelling approach (e.g. Dempster-Shafer) that would over-predict,protect more sites,and thereby minimize the risk of missing critical habitat in conservation plans.     

Relative accuracy of spatial predictive models for lynx Lynx canadensis derived using logistic regression-AIC,multiple criteria evaluation and Bayesian approaches

We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems （GIS） -based approaches： logistic regression and Akaike＇s Information Criterion （AIC）, Multiple Criteria Evaluation （MCE）, and Bayesian Analysis （specifically Dempster-Shafer theory）. We used lynx Lynx canadensis as our focal species, and developed our environment relationship model using track data collected in Banff National Park, Alberta, Canada, during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified （overall accuracy）, the failure to predict a species where it occurred （omission error） and the prediction of presence where there was absence （commission error）. Our overall accuracy showed the logistic regression approach was the most accurate （74.51%）. The multiple criteria evaluation was intermediate （39.22%）, while the Dempster-Shafer （D-S） theory model was the poorest （29.90%）. However, omission and commission error tell us a different story： logistic regression had the lowest commission error, while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least, the logistic regression model is optimal. However, where sample size is small or the species is very rare, it may also be useful to explore and/or use a more ecologically cautious modelling approach （e.g. Dempster-Shafer） that would over-predict, protect more sites, and thereby minimize the risk of missing critical habitat in conservation plans .     

Combined effect of divergent selection for breast muscle ultimate pH and dietary amino acids on chicken performance,physical activity and meat quality

Responses to changes in dietary Lys and other essential amino acid (AA) concentrations were evaluated in 480 male and female broilers originating from two lines divergently selected for high (pHu+) or low (pHu−) ultimate pH (pHu) of breast muscle. The two genetic lines were fed with two grower isoenergetic diets differing in both true digestible Lys (control=10.2 g/kg and experimental=7.0 g/kg) and amounts of other essential AA calculated in relation to Lys, which were sufficient for the control diet or in excess for the experimental diet. There were six repetitions per treatment. Birds were weighed individually at days 0, 21, 28 and 43. Feed consumption was recorded per pen and feed conversion was calculated over the growing period. The physical activity and walking ability of broilers were recorded during the whole rearing period. Breast and leg yield, and abdominal fat percentage were measured at 43 days of age, as were pHu, color, drip and cooking loss, Warner–Bratzler shear force, and curing-cooking yield of the breast Pectoralis major and pHu of the thigh Sartorius muscle. Divergent selection greatly affected most breast meat quality traits without significantly changing growth rate or feed efficiency. When subjected to a variation in dietary intake of AA, birds from the two genotypes responded in a similar way in terms of animal’s growth, feed efficiency, body composition and meat quality traits. Although line and diet did not affect physical or feeding activities of the broilers, a significant effect of line-by-diet interaction was observed on gait score. Contrary to the pHu− birds, the walking ability of pHu+ birds was impaired when fed the control diet that favored growth and breast muscle development and limited storage of carbohydrate in muscle.