Activity patterns of ectothermic animals are affected by weather, time of day, and season, but quantifying these effects can be logistically challenging. We used an automated radio telemetry system to quantify Ornate Box Turtle (Terrapene ornata) activity patterns for two years in northern Illinois. Continuously collected activity data were paired with meteorological data collected from the site to determine factors influencing turtle behavior. Temperature, relative humidity, rain, year, month, time of day, and reproductive status affected activity levels. Increased activity levels corresponded with rain events, and males were generally more active than females, especially during spring and late summer. Overall, turtles were less active during an uncharacteristically warm and dry year compared to a year with conditions that were closer to the long-term average. Bimodal daily activity patterns have been reported in more southerly populations, and we found similar patterns near the species’ northern range l...

The Eastern and Florida Chicken Turtle subspecies (Deirochelys reticularia reticularia and D. r. chrysea) exhibit nesting cycles that are atypical for aquatic North American turtles, with nesting occurring in the fall, winter, and early spring. The reproductive cycles of the western subspecies (D. r. miaria), however, have not been studied. Therefore, we monitored the reproductive status of female D. r. miaria in 2012 and 2013. In our populations, both male and female D. r. miaria were inactive on land from mid-July to mid-March. This differs from the activity patterns of the other subspecies in which females typically remain active into the fall and sometimes winter. Also, in contrast to the nesting patterns of D. r. reticularia and D. r. chrysea, female D. r. miaria developed follicles in the spring and summer (March–July) and contained eggs in the summer (May–July). Therefore, we conclude that the Western Chicken Turtle exhibits a nesting pattern that is more similar to that of other sympatric species in the family Emydidae than it is to other subspecies of D. reticularia.

We measured temperature-induced differences in metabolic rates and growth by embryos of three turtle species,Macrochelys temminckii, Trachemys scripta, andApalone spinifera, at different, constant, temperatures. Oxygen consumption rate (VO₂) was measured during development and used to characterize changes in metabolism and calculate total O₂consumption. Results from eggs incubated at different temperatures were used to calculate Q₁₀s at different stages of development and to look for evidence of metabolic compensation. Total O₂consumption over the course of incubation was lowest at high incubation temperatures, and late-term metabolic rate Q₁₀s were <2 in all three species. Both results were consistent with positive metabolic compensation. However, incubation temperature effects on egg mass-corrected hatchling size varied among species.Apalone spiniferahatchling mass was unaffected by temperature, whereasT. scriptamass was greatest at high temperatures andM. temminckiimass was lowest at high temperatures. Hatchling mass : length relationships tended to correlate negatively with temperature in all three species. Although we cannot reject positive metabolic compensation as a contributor to the observed VO₂patterns, there is precedence for drawing the more parsimonious conclusion that differences in yolk-free size alone produced the observed incubation temperature differences without energetic canalization by temperature acclimation during incubation.

Natural resource agencies routinely collect handwritten and audio field data that must be transcribed before analysis, creating a persistent bottleneck in data management. Recent advances in artificial intelligence (AI) may help automate portions of this work, yet their utility in routine fisheries applications remains largely untested. We evaluated commercially available AI-assisted workflows against standard manual transcription. The evaluation considered total processing time and initial transcription accuracy. Across the AI pilot datasets, initial accuracy averaged 97% for handwritten records and 95% for audio recordings. After quality control, the AI-assisted workflows reduced total processing time by approximately 37% for handwritten transcription and 49% for audio transcription. The larger operational datasets produced reductions of approximately 30% and 57%, respectively. Initial accuracy for the larger handwritten dataset declined to 82.3%, but all identified errors were corrected during quality control. These findings show that commercially available AI can improve a time-intensive aspect of natural resource data management without specialized programming or custom software. Incorporating AI-assisted transcription into existing workflows can reduce administrative workload and move field observations into management and research applications sooner.