Evaluating trade-offs in spatial versus temporal replication when estimating avian community composition and predicting species distributions

Species distribution modeling is important for predicting species responses to environmental change, but model accuracy can be limited by a lack of data in remote areas. Hierarchically stratified surveys (cluster sampling) offer an efficient approach to sampling in remote areas, but an appropriate balance is needed between cost efficiency and statistical independence. The cost-effectiveness of cluster sampling likely varies with temporal sampling intensity (e.g., single vs. multiple repeat samples) due to differences in spatial autocorrelation. Our aim was to assess the trade-offs between spatial and temporal replication and optimize sampling in which temporal replication occurs. We used bootstrap resampling to create alternative designs from multi-species avian point-count and autonomous recording unit data. We varied the number of primary sample units (PSUs), secondary sampling units per PSU (SSUs), and temporal repeat samples (i.e., visits) at each SSU. We fit species accumulation curves to examine how spatial and temporal replication influenced species accumulation. We split data into spatially independent model training and validation datasets and fit species distribution models (SDM) for 47 species using generalized linear models and examined how prediction accuracy changed with sampling intensity to examine the cost-benefit trade-offs between spatial versus temporal replication within PSUs. We found that spatial and temporal replication were partially redundant and adding more visits had less influence on predictive accuracy when there were more SSUs and vice versa. The cost-benefit of increasing spatial replication within PSUs varied with the costs of accessing SSUs. The optimal number of SSUs per PSU varied with both temporal replication and the number of unique PSUs sampled. In general, using <= 3 SSUs per PSU produced the most accurate SDM predictions when the number of PSUs was high. When the number of PSUs was low and/or SSU costs were low, increasing clustering within PSUs can optimize sampling.