Bias is widespread in most ecological data, especially those collected by harvesting or hunting which are, by nature, selective (Ginsberg & Milner-Gulland 1994
; Noss 1999
; Laurian et al. 2000
). Although introduced bias is routinely corrected for in fisheries systems (Murphy & Willis 1996
), similar bias is often ignored in terrestrial systems. The present study, therefore, seeks to highlight this issue by using a hunting-derived dataset to examine the bias introduced by particular hunting methodologies.
Our results demonstrate that the hunting type used to collect the data has a significant influence upon the apparent relationship between weight and age. This bias is most pronounced (i.e. the differences between predicted weights for different hunting types are greatest) in young or adult deer rather than in very old deer. It is clear, therefore, that a hunter's objective has a significant effect on the characteristics of the shot animals and on selection pressure since larger animals are more likely to be shot at a younger age.
The objective of trophy-stalkers is to obtain the best (i.e. largest) trophy. Therefore, only adult animals (older than four years) are shot and there appears to be a threshold weight so that the weight of animals shot using this method changes little between four and twelve years of age. On the other hand, although montería hunters have the same aims as trophy-stalkers, they are apparently not as good at selecting for weight. This is probably because they are typically confronted with a larger number of animals that they can shoot but have less time to make an assessment and selection. Therefore, the animals they select are not as heavy as those shot using trophy hunting at young ages, and some young (less than 4 years) and small animals are shot, presumably in order to fill their quota. Finally, animals of every age are selected with bycatch and management hunting methods and, therefore, a more complete population sample may be possible. However, since both bycatch and management hunting tend to eliminate low-quality deer for every age, the sample is certainly not random. These selection pressures have clear consequences for wildlife management (Coltman et al. 2003
When interpreting data collected by such an invasive method as hunting it is worth considering that simply the collection of the data can influence the system. In natural systems there is selection pressure to be large/heavy to ensure breeding success and survival (Saether 1997
). Furthermore, in many systems anthropogenic hunting represents an additional source of selective pressure (Ginsberg & Milner-Gulland 1994
; Laurian et al. 2000
). The direction and strength of these pressures depend on the hunting methodology. For example, when management hunters shoot the weak/small individuals there may be a large positive selection on body mass. A more common situation in private Spanish ranches managed for profit, is that commercial hunting is more common than management hunting of males. It is clear that the balance of these natural and anthropogenic selection pressures may influence both the demographic structure and the dynamics of the system and should therefore be taken into account when comparing systems in different locations with differing selection pressures.
Although recent studies have shown that senescence occurs in red deer males above 10 or 11 years old (Mysterud et al. 2001
; Carranza et al. 2004
), we did not consider it in our analyses because 95.7% of the animals were under 11 years old. It is also pertinent to note that data were scarce for very young ages and, therefore, care should be taken not to over-interpret the model within these areas of parameter space.
Nevertheless, our results show that the apparent functional relationship between age and weight depends upon the methodology used to collect the data. Weight and body size are typically highly correlated and have a huge influence on individual fitness (Saether 1997
). Clearly, bias introduced by a failure to control for hunting methods in the estimation of weight-related effects may have significant consequences for the interpretation of analyses involving weight or correlated traits such as breeding success (Clutton-Brock et al. 1982
; Langvatn et al. 1996
; Yoccoz et al. 2002
; Bonenfant et al. 2003
). We therefore urge researchers to explore methods to identify and correct for such bias in their data. We realize, however, that such bias may not be constant through time or space and, therefore, that correction over such scales may not be straightforward.