Species Distribution Models (SDMs) are commonly used statistical tools in conservation biology, global change assessment, and reserve prioritization. Correlative SDMs relate species occurrences to environmental conditions, and it is common to model heterogeneity in the data with coarse-scale spatial and temporal predictors. However, this approach neglects the fine-scale environmental conditions experienced by most organisms. Further, most SDMs use occurrence data from short-term studies but make long-term predictions of future conditions. We compare four modeling frameworks that varied the temporal extent (short-term [1 year] versus long-term [10 years]) and resolution of environmental data (fine versus coarse). We expected that long-term data and finer temporal resolution of environmental variables would provide more accurate model predictions because they integrate variability in population sizes under varying microclimatic conditions. We built SDMs for 37 bird species in the H. J. Andrews Experimental Forest, Cascade Range, Oregon (USA). We used a 10-year (2010-2019) time series of annual observations during breeding season across 184 sites as response variables and gridded maps of hourly below forest canopy microclimate temperatures and LiDAR-derived vegetation variables as predictors. We evaluated the interannual transferability of long- versus short-term models and fine versus coarse-resolution temperature models; we also tested whether species’ functional traits affected the performance of models. Temporally dynamic (long-term) models with higher-resolution microclimate data outperformed static and short-term approaches in terms of performance (AUC difference ~ 0.10, TSS difference ~ 0.12). Model performance and similarity between spatial predictions were higher for dynamic rather than static models, especially for migratory species. Models for small bird species performed better as temporal resolution increased, whereas for long-lived species with larger body sizes, dynamic approaches performed similarly to static models. We advocate for increased use of fine-scale, long-term data in SDMs to boost the performance and reliability of future predictions under global change.

Species detection using eDNA is revolutionizing global capacity to monitor biodiversity. However, the lack of regional, vouchered, genomic sequence information—especially sequence information that includes intraspecific variation—creates a bottleneck for management agencies wanting to harness the complete power of eDNA to monitor taxa and implement eDNA analyses. eDNA studies depend upon regional databases of mitogenomic sequence information to evaluate the effectiveness of such data to detect and identify taxa. We created the Oregon Biodiversity Genome Project to create a database of complete, nearly error-free mitogenomic sequences for all of Oregon’s fishes. We have successfully assembled the complete mitogenomes of 313 specimens of freshwater, anadromous, and estuarine fishes representing 24 families, 55 genera, and 128 species and lineages. Comparative analyses of these sequences illustrate that many regions of the mitogenome are taxonomically informative, that the short (~150 bp) mitochondrial “barcode” regions typically used for eDNA assays do not consistently diagnose for species, and that complete single or multiple genes of the mitogenome are preferable for identifying Oregon’s fishes. This project provides a blueprint for other researchers to follow as they build regional databases, illustrates the taxonomic value and limits of complete mitogenomic sequences, and offers clues as to how current eDNA assays and environmental genomics methods of the future can best leverage this information.

Species detection using eDNA is revolutionizing the global capacity to monitor biodiversity. However, the lack of regional, vouchered, genomic sequence information—especially sequence information that includes intraspecific variation—creates a bottleneck for management agencies wanting to harness the complete power of eDNA to monitor taxa and implement eDNA analyses. eDNA studies depend upon regional databases of complete mitogenomic sequence information to evaluate the effectiveness of such data to differentiate, identify and detect taxa. We created the Oregon Biodiversity Genome Project working group to utilize recent advances in sequencing technology to create a database of complete, near error-free mitogenomic sequences for all of Oregon’s resident freshwater fishes. So far, we have successfully assembled the complete mitogenomes of 313 specimens of freshwater fish representing 7 families, 55 genera, and 129 (88%) of the 146 resident species and lineages. Our comparative analyses of these sequences illustrate that the short (~150 bp) mitochondrial “barcode” regions typically used for eDNA assays are not consistently diagnostic for species-level identification and that no single region is best for metabarcoding Oregon’s fishes. However, often-overlooked intergenic regions of the mitogenome such as the D-loop have the potential to reliably diagnose and differentiate species. This project provides a blueprint for other researchers to follow as they build regional databases. It also illustrates the taxonomic value and limits of complete mitogenomic sequences, and how current eDNA assays and the “PCR-free” environmental genomics methods of the future can best leverage this information.