Designing a large-scale track-based monitoring program to detect changes in species distributions in arid Australia.

Monitoring trends in animal populations in arid regions is challenging due to remoteness and low population densities. However, detecting species' tracks or signs is an effective survey technique for monitoring population trends across large spatial and temporal scales. In this study, we developed a simulation framework to evaluate the performance of alternative track-based monitoring designs at detecting change in species distributions in arid Australia. We collated presence-absence records from 550 2-ha track-based plots for 11 vertebrates over 13 years and fitted ensemble species distribution models to predict occupancy in 2018. We simulated plausible changes in species' distributions over the next 15 years and, with estimates of detectability, simulated monitoring to evaluate the statistical power of three alternative monitoring scenarios: (1) where surveys were restricted to existing 2-ha plots, (2) where surveys were optimized to target all species equally, and (3) where surveys were optimized to target two species of conservation concern. Across all monitoring designs and scenarios, we found that power was higher when detecting increasing occupancy trends compared to decreasing trends owing to the relatively low levels of initial occupancy. Our results suggest that surveying 200 of the existing plots annually (with a small subset resurveyed twice within a year) will have at least an 80% chance of detecting 30% declines in occupancy for four of the five invasive species modeled and one of the six native species. This increased to 10 of the 11 species assuming larger (50%) declines. When plots were positioned to target all species equally, power improved slightly for most compared to the existing survey network. When plots were positioned to target two species of conservation concern (crest-tailed mulgara and dusky hopping mouse), power to detect 30% declines increased by 29% and 31% for these species, respectively, at the cost of reduced power for the remaining species. The effect of varying survey frequency depended on its trade-off with the number of sites sampled and requires further consideration. Nonetheless, our research suggests that track-based surveying is an effective and logistically feasible approach to monitoring broad-scale occupancy trends in desert species with both widespread and restricted distributions.

Towards a continent-wide ecological site condition database using calibrated expert evaluations.

A cost-effective way of undertaking comprehensive, continental-scale, assessments of ecological condition is needed to support large-scale conservation planning, monitoring, reporting, and decision-making. Currently, cross-jurisdictional inconsistency in assessment methods limits the capacity to scale-up monitoring. Here we present a novel way to build a coherent continent-wide site-level ecological condition dataset, using cross-calibration methods to integrate assessments from many observers. We focus on the use of condition assessments from individual expert observers, a currently untapped resource. Our approach has two components: (1) a simple online tool that captures expert assessments at specific locations; (2) a process of calibrating and rescaling disparate expert evaluations that can be applied to the data to provide a consistent dataset for use in conservation assessments. We describe a pilot study, involving 28 experts, who contributed 314 individual site condition assessments across a wide range of ecosystems and regions throughout continental Australia. A correction factor for each expert was used to rescale the contributed site condition assessment scores, based on a set of 77 photographic images, each scored for their condition by multiple experts, using a linear mixed model. Our approach shows strong promise for delivering the volumes of data required to develop continental-scale reference libraries of site condition assessments. Although developed from expert elicitation, the approach could also be used to harmonize the collation of existing condition datasets. The process we demonstrate can also facilitate online citizen scientists to make site condition assessments that can be cross-calibrated using contributed images.