Optimising monitoring for trend detection after 16 years of woodland‐bird surveys

Long-term biodiversity monitoring programs provide important information about species' trajectories and broader environmental change. Often constrained by funding and organisational capability and commitment, monitoring programs need to be optimised to maximise ecological and economic efficiencies, as part of sound adaptive management. The monitoring design requirements for detecting biodiversity trends, across assemblages of species with different traits, can be informed by historical datasets. Using data from a landscape-scale (c. 2,500 km) bird monitoring program encompassing 151 sites visited three times annually over 16 years, we used resampling to simulate different monitoring designs. We quantified the capacity of modified monitoring regimes to detect population trends for 65 bird species with different densities, detectabilities and specialisations. The majority (58%) of species exhibited a significant decline in relative abundance, with the ability to detect trends proportional to the length of the time series used for analysis. The percentage of trends detected decreased as survey sites or sessions were dropped from the monitoring dataset. Statistically significant trends remained undetected for an additional 2.5 species for every 10% of sites excluded randomly from the program. As monitoring effort was reduced, the precision of trend estimates for rare species was particularly compromised. Conducting bird surveys every second year would produce better results than an equivalent reduction in effort achieved by surveying only half the sites each year, but could compromise the sustainability of the program. If the number of survey sites were reduced, trend detection would be optimised by retaining the spatial extent of the surveys (i.e. by dropping sites from well-surveyed regions rather than excluding outlying, isolated sites), but the cost savings of this approach would be small. Synthesis and applications. Reduced monitoring effort will compromise trend detection for rare species or species that are difficult to observe, and declining species that will soon become rare. Consequently, monitoring effort that is considered ‘surplus’ today could provide critical data for detecting species-level trends and prioritising conservation interventions in the future. Further, sampling efficiencies are not all-important; we must also consider the impacts of survey design modification on the social and political sustainability of existing monitoring programs.