Management of threatened species relies on knowledge of their distributions and abundances. This has led to an explosion of techniques for modelling species’ distributions (Guisan & Zimmermann 2000; Ferrier et al. 2002). The cost of collecting field data, in terms of time, expense and necessary resources, can be large and may substantially reduce the budget available for management (Field et al. 2004; Seoane, Bustamante & Diaz-Delgado 2005). While objective confirmation through appropriately designed field sampling is desirable, it is not always possible within the time and budget constraints of a project. Where there is insufficient field data, an efficient source of less expensive information can be expert knowledge gained from extensive experience. Expert knowledge has been increasingly incorporated into management recommendations and practices in a wide variety of fields (Yamada et al. 2003; Martin et al. 2005; Seoane et al. 2005).
Expert knowledge can be incorporated into species’ distribution models using a Bayesian statistical framework (Low Choy, O’Leary & Mengersen 2009). Information from experienced experts, including their uncertainty, can be quantified to construct a ‘prior’ probability distribution for model parameters (e.g. regression coefficients). Using Bayes’ theorem we can update prior estimates of model parameters using other data to form ‘posterior’ estimates (Ellison 2004; O’Hagan et al. 2006). Prior estimates have more effect when data are limited and highly variable. Priors reflecting vague or no previous knowledge are ‘non-informative’ and can prove problematic with limited data. ‘Informative priors’, based on elicited expert knowledge, can also be used so that results utilize all available relevant species information (Morgan & Hemrion 1990; Ellison, Gregory & Hardcastle 1998; Ellison 2004).
Expert opinion has successfully been used as a priori information for developing ecological models (Yamada et al. 2003; Martin et al. 2005; O’Leary et al. 2008). McCarthy & Masters (2005) found combining expert opinion and data avoided the need for 2 years of additional sampling. In contrast, Pearce et al. (2001) and Seoane et al. (2005) found little improvement in the predictive power of models when expert opinion was used at different stages of model-building within a classical statistical approach, including modifying species’ distribution models a posteriori. Although many studies demonstrate valuable contributions by expert opinion to data analysis, only a few have discussed the sourcing of suitable experts for informing ecological models or compared expert opinions from different regions (O’Neill et al. 2008; Hurley, Rapaport & Johnson 2009). In particular, we pose the questions: (i) How does combining expert opinion with empirical data improve species’ distribution models? (ii) Does the source of expertise make a difference to expert assessments?
We addressed these questions by modelling the distribution of a nationally threatened marsupial from eastern Australia, the brush-tailed rock-wallaby Petrogale penicillata Gray 1827. The species is declining and is listed as threatened across its range. Hence, there is an urgency to implement management actions to stop and reverse the decline, utilizing all information currently available. Managers need to identify potential habitat to protect the species from common threats, such as introduced predators and wildfires. Brush-tailed rock-wallabies live in small colonies in naturally fragmented habitat in rugged terrain incorporating cliffs or boulder piles. A dominant male usually defends a number of females that occupy suitable rock refuges during the day to escape from extremes of weather and potential threats (Short 1982; Jarman & Bayne 1997; Murray et al. 2008). Collecting adequate field data in such challenging terrain is costly and time-consuming. Expert knowledge provides an alternative source of data for this species to supplement traditional field sampling, which is limited due to time and monetary constraints.
In this study, we further the work performed by O’Neill et al. (2008) in assessing uncertainties in expert opinions. We use an extensive field data set and a number of habitat variables to compare species’ distribution predictions of brush-tailed rock-wallaby distributions derived from field data alone together with predictions derived from Bayesian models using the same field data combined with expert knowledge obtained over the same area and quantified as priors. We used a newly designed elicitation tool with readily available geographical information system (GIS) data to interview multiple experts from two regions and evaluate how their assessments affect predictions in the adjoining less familiar region. Furthermore, we offer suggestions for conservation of the brush-tailed rock-wallaby and for choosing experts best suited to assessment in the region of interest.