Oxygen environment and metabolic oxygen demand predictably interact to affect thermal behavior in a lizard, Sceloporus occidentalis

3D printed models are an accurate, cost-effective, and reproducible tool for quantifying terrestrial thermal environments

Predicting ecological responses to rapid environmental change has become one of the greatest challenges of modern biology. One of the major hurdles in forecasting these responses is accurately quantifying the thermal environments that organisms experience. The distribution of temperatures available within an organism's habitat is typically measured using data loggers called operative temperature models (OTMs) that are designed to mimic certain properties of heat exchange in the focal organism. The gold standard for OTM construction in studies of terrestrial ectotherms has been the use of copper electroforming which creates anatomically accurate models that equilibrate quickly to ambient thermal conditions. However, electroformed models require the use of caustic chemicals, are often brittle, and their production is expensive and time intensive. This has resulted in many researchers resorting to the use of simplified OTMs that can yield substantial measurement errors. 3D printing offers the prospect of robust, easily replicated, morphologically accurate, and cost-effective OTMs that capture the benefits but alleviate the problems associated with electroforming. Here, we validate the use of OTMs that were 3D printed using several materials across eight lizard species of different body sizes and living in habitats ranging from deserts to tropical forests. We show that 3D printed OTMs have low thermal inertia and predict the live animal's equilibration temperature with high accuracy across a wide range of body sizes and microhabitats. Finally, we developed a free online repository and database of 3D scans (https://www.3dotm.org/) to increase the accessibility of this tool to researchers around the world and facilitate ease of production of 3D printed models. 3D printing of OTMs is generalizable to taxa beyond lizards. If widely adopted, this approach promises greater accuracy and reproducibility in studies of terrestrial thermal ecology and should lead to improved forecasts of the biological impacts of climate change.

Biogeographic inferences across spatial and evolutionary scales

The field of biogeography unites landscape genetics and phylogeography under a common conceptual framework. Landscape genetics traditionally focuses on recent-time, population-based, spatial genetics processes at small geographical scales, while phylogeography typically investigates deep past, lineage- and species-based processes at large geographical scales. Here, we evaluate the link between landscape genetics and phylogeographical methods using the western fence lizard (Sceloporus occidentalis) as a model species. First, we conducted replicated landscape genetics studies across several geographical scales to investigate how population genetics inferences change depending on the spatial extent of the study area. Then, we carried out a phylogeographical study of population structure at two evolutionary scales informed by inferences derived from landscape genetics results to identify concordance and conflict between these sets of methods. We found significant concordance in landscape genetics processes at all but the largest geographical scale. Phylogeographical results indicate major clades are restricted to distinct river drainages or distinct hydrological regions. At a more recent timescale, we find minor clades are restricted to single river canyons in the majority of cases, while the remainder of river canyons include samples from at most two clades. Overall, the broad-scale pattern implicating stream and river valleys as key features linking populations in the landscape genetics results, and high degree of clade specificity within major topographic subdivisions in the phylogeographical results, is consistent. As landscape genetics and phylogeography share many of the same objectives, synthesizing theory, models and methods between these fields will help bring about a better understanding of ecological and evolutionary processes structuring genetic variation across space and time.