17 – Saving our Species^2272013c). Also, there have been frequent criticisms and recommendations in the
scientific literature related to improving systems for linking expenditure to
outcomes, evaluating the impact of threatened species management, and
improving the capacity of monitoring programs to adequately detect and respond
to population change (e.g. Hajkowicz 2009; Bottrill et al. 2011; Lindenmayer et al.
2013; Legge 2015).
The challenge for a program covering such a large and diverse set of species
and habitats as SoS is to provide a framework that: (i) facilitates scientifically
rigorous yet cost-effective monitoring; (ii) evaluates outcomes using standardised,
comparable metrics; and (iii) can regularly document those outcomes in a way that
is transparent and useful for informing decision making and adaptive learning. To
meet this challenge the design of the SoS framework is underpinned by three key
objectives:
● (^) linking expenditure to outcomes: identifying explicitly the amount invested
in particular species, sites and actions, and evaluating related outcomes against
clear benchmarks or targets (return on investment)
● (^) informing decision making: focusing data collection to where it is most
likely to reduce uncertainty and improve decisions with respect to
management methods (adaptive management) or resource allocation
(prioritisation models)
● (^) maximising cost-effectiveness: ensuring that the allocation of resources to
monitoring is proportionate to the value of the information to be gained (in
terms of demonstrating return on investment or informing decision making).
Monitoring
Monitoring the outcomes of conservation projects under SoS is structured
according to a generalised process model or logic chain (Fig. 17.1). Monitoring is
designed to test the assumptions at each step in the model. If monitoring data
support each of these assumptions for a particular project, the project is considered
on track to meet long-term objectives. If the data violate one or more assumptions,
this indicates that current management is ineffective or insufficient, or our
knowledge of the system’s drivers is incomplete.
To ensure that monitoring data can be relied on to inform such assessments,
and those assessments are consistent and comparable across multiple, highly
variable projects, SoS requires data be collected and interpreted in a rigorous
manner. An ideal monitoring program for any given threatened species would
incorporate all of the following features:
● (^) be based on a conceptual model validated with empirical data that has good
predictive power