17 – Saving our Species^229SoS attempts to balance these competing priorities (or at least ensure they are
valued consistently between projects across the program) via a governance process
that includes review of project monitoring plans by a technical reference group
made up of ecological, threat management and statistical experts.
Ultimately, given the variable nature of species and habitats across SoS, achieving
consistency in the rigour of monitoring regimes means that the costs of monitoring
will vary among projects. For example, monitoring cryptic or mobile fauna is
generally more costly than undertaking f lora surveys. The costs of implementing
monitoring and management (C) are incorporated, along with a project’s benefit (B)
and likelihood of success (L), into a score (P = B × L/C), which is use to rank projects
for investment (Project Prioritisation Protocol; Joseph et al. 2009).
Ensuring that monitoring data are interpreted appropriately requires a
methodology for documenting the confidence associated with data collection and
analysis. For example, quantitative data might be collected during a monitoring
program that can detect significant responses to management with a high level of
statistical power, while data collected from other monitoring programs might be
based on a manager’s qualitative assessment that values have improved over time.
In both cases, each project could be reported as meeting their respective objectives,
but the confidence associated with each evaluation will be different. Under SoS, all
monitoring data are given a categorical measure of confidence (high, moderate or
low), related to the power of the monitoring program to detect change, the richness
of the data collected and the scientific rigour of analysis.
Evaluation
Evaluation of management outcomes provides information about return on
investment, which in addition to justifying the program to government and the
community, informs decisions about how and where to invest in order to maximise
those returns. There are two key areas of decision making where these data can
improve outcomes: (1) improving the evidence base for particular management
actions (i.e. adaptive management), thereby improving their effectiveness; or (2) by
improving prioritisation models (e.g. increasing the accuracy of data on the benefit,
likelihood of success and cost of management), thereby improving the outcome of
resource allocation decisions.
Using conceptual models
The only way to evaluate the effectiveness of management properly is by
comparison with a reference or benchmark (Burgman et al. 2012). Under SoS,
benchmarks are expressed as short- and long-term targets for the response of
populations and threats to management. These targets are determined on a