Many ecological studies are characterized by paucity of hard data. Statistical analysis in such situations leads to flat likelihood 
functions and wide confidence intervals. However, although there is paucity of hard data, expert knowledge about the phenomenon under 
study is many time available. Such expert opinion may be used to strengthen the statistical inference in these situations. Subjective 
Bayesian is one approach to incorporate expert opinion in statistical studies. This approach, aside from the subjectivity, also faces 
operational problems. Elicitation of the prior is the most difficult step. Another is the lack of a precise quantitative definition of 
what characterizes an expert. In this paper, we discuss a different approach to incorporating subjective expert opinion in statistical 
analyses. We argue that it is easier to elicit data than to elicit a prior.  Such elicited data can then be used to supplement the hard, 
observed data to possibly improve precision of statistical analyses. The approach suggested here also leads to a natural definition of 
what constitutes a useful expert. We define a useful expert as one whose opinion adds information over and above what is provided by the 
observed data.  This can be quantified in terms of the change in the Fisher information before and after using the expert opinion. One 
can, thus, avoid the real possibility of using an expert opinion that adds noise, instead of information, to the hard data. We illustrate 
this approach using an ecological problem of modeling and predicting occurrence of species. An interesting outcome of this analysis is 
that statistical thinking helps discriminate between a useful expert and a not so useful expert; expertness need not be decided purely on 
the basis of experience, fame or such qualitative characteristics.