188 Monitoring Threatened Species and Ecological Communities
Adaptive monitoring uses scientific design principles to investigate well-considered
questions within a conceptual framework in a way that assists management
(e.g. Lindenmayer et al. 2011). In strategic adaptive monitoring (SAM), the extent
and type of monitoring may be varied in scope and detail, over space and time, in a
deliberate and planned manner. SAM has several distinct stages. The initial stage
establishes baseline and reference values for a broad range of factors that help
characterise the species and/or ecosystem and potential agents of change. The
baseline sampling regime is at a sufficiently detailed resolution over space and time
(e.g. seasons) to develop an adequate and representative reference. Some samples
may be preserved for potential future analysis when sufficient resources are
secured or they become a higher priority. This stage also presents an opportunity
to validate our understanding of the population(s)/ecosystem(s) upon which the
SAM is focused. In the second, routine, stage, the amount of monitoring is reduced
to focus on the priorities as best as can be managed with the resources available. If
and when the results of the routine monitoring demand it, targeted increases in
monitoring effort can take advantage of pre-planned protocols and pre-existing
infrastructure on standby. This may include the reactivation of some sites and
sampling regimes used in the baseline stage that were ‘moth-balled’ in the routine
stage of the monitoring program.
SAM is a pragmatic approach that aims to collect the most important data at
least some of the time. The extent and nature of the variation in data collection can
be specific to each factor. For example, the monitoring of population trends may be
done at a higher spatial resolution and/or frequency than more detailed data
collection on the demographic characteristics and covariates that might explain
the mechanisms or drivers of population change. However, setting the appropriate
data sampling regimes needs to be informed by the limitations they will impose on
the inferential power of the subsequent analyses and whether these limitations are
broadly acceptable. For example, there may be minimum rates and magnitudes of
population change that are considered necessary for a monitoring regime to detect
with a certain level of confidence (e.g. reveal a <20% annual change in population
size with >80% confidence).
Another important element of SAM is the high-level integration and
collaboration between monitoring, scientific research and management. For
example, monitoring can inform research and management, while research can be
integrated into and help improve the quality of monitoring, and management can
clarify the priorities that will deliver practical and meaningful outcomes and may
be more responsive to the results of monitoring and research with which they have
been directly involved. SAM also can take advantage of changes in monitoring
capability that may occur over time, such as periodic increases of resources
available to monitoring.
Although monitoring in the UWR was not originally designed as SAM, it
can demonstrate the principles and advantages of SAM. Once the declines of