Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While location error can often be ignored in coarsely sampled data, fine-scale data require much more care, and tools to do this have been lacking. Current approaches to dealing with location error largely fall into two categories—either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. Unfortunately, both of these approaches have serious flaws. Here, we provide a general framework to account for location error in the analysis of animal tracking data, so that their potential can be unlocked. We apply our error-model-selection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices are used to track a wide range of animal species comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. Then, using empirical data on tracked individuals from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. We furthermore demonstrate how error-informed analyses on calibrated tracking data can be necessary to ensure that estimates are accurate and insensitive to location error, and allow researchers to use all of their data. Because error-induced biases depend on so many factors—sampling schedule, movement characteristics, tracking device, habitat, etc.—differential bias can easily confound biological inference and lead researchers to draw false conclusions.

Population monitoring is a fundamental component of wildlife management, and is necessary to track site-and regional-level status and recovery of species of conservation concern. The wood turtle (Glyptemys insculpta) is a species of conservation concern for federal and state agencies because of population declines across the species' range. We developed and tested a survey and analysis design to assist agencies in the Upper Midwest, USA, with establishment of long-term monitoring programs for wood turtle populations. In spring of 2016, we conducted 8 replicate population surveys at 8 candidate long-term monitoring sites in northeastern Minnesota, USA. Using field survey data and simulation models, we assessed the influence of distance from river surveyed, number of survey replications, and number of sites on abundance estimates; we also delineated important survey covariates and compared demographic estimates based on distance from river surveyed. We estimated site-level abundances and compared survey designs using a multinomial N-mixture model that included a removal sampling observation process. Mean abundance estimates were similar when surveying 2 transects (i.e., the river-land interface to $25 m inland) or 4 transects (i.e., the river-land interface to $55 m inland), but decreasing the survey distance from river reduced the precision of estimates. Mean abundance estimates were similar with !6 replications. Air temperature was an important predictor of survey-specific detection probability, with maximum detectability at 19À238C. Sex ratio and mean carapace length did not differ based on whether we surveyed 2 or 4 transects, and percentage of individuals by size class was nearly identical between the sampling designs. Simulations indicated that 75% of mean abundance estimates were within AE8% of true abundance when !15 sites were surveyed. The wood turtle survey and analysis design we developed and tested was effective for estimating abundance of wood turtle populations in northeastern Minnesota, and we encourage its use as a template for wood turtle monitoring programs in the Upper Midwest. Ó 2017 The Wildlife Society.

Global positioning system (GPS) telemetry units are now small enough to be deployed on terrestrial and semi-aquatic turtles. Many of these GPS units use snapshot technology which collects raw satellite and timestamp data during brief periods of data recording to minimize size. We evaluated locations from snapshot GPS units in stationary tests and on wood turtles (Glyptemys insculpta) in northeastern Minnesota. Stationary GPS units were placed in wood turtle habitat to evaluate location accuracy, fix success rate, and directional bias. The GPS fix success rate and accuracy were reduced in closed canopy conditions and when the stationary GPS unit was placed under a log to simulate wood turtle hiding behavior. We removed GPS location outliers and used a moving average calculation to reduce mean location error in stationary tests from 27 m (SD = 38) to 10 m (SD = 8). We then deployed GPS units and temperature loggers on wood turtles and collected 122,657 GPS locations and 242,781 temperature readings from 26 turtles from May to September 2015 and 2016. Location outliers accounted for 12% of locations when the GPS receiver was on a turtle. We classified each wood turtle location based on the GPS location and by comparing temperature profiles from river, sun, and shaded locations to the temperature logger on the turtle. We estimated that wood turtles were on land 68% (SD = 12) of the time from May to September. The fix success rate for land locations was 38% (SD = 9), indicating that wood turtles often use habitats with obstructed views of the sky. Mean net daily movement was 55 m (SD = 192). Our results demonstrate that snapshot GPS units and temperature loggers provide fine-scale GPS data useful in describing spatial ecology and habitat use of semi-aquatic turtles.

Invasive fish predators are an important factor causing amphibian declines and may have direct and indirect effects on amphibian survival. For example, early non-lethal exposure to these stressors may reduce survival in later life stages, especially in biphasic species. In amphibians, the glucocorticoid hormone corticosterone is released by the hypothalamo–pituitary–interrenal axis (HPI), as an adaptive physiological response to environmental stressors. The corticosterone response (baseline and response to acute stressors) is highly flexible and context dependent, and this variation can allow individuals to alter their phenotype and behavior with environmental changes, ultimately increasing survival. We sampled larvae of the spring salamander (Gyrinophilus porphyriticus) from two streams that each contained predatory brook trout (Slavelinus fontinalis) in the lower reaches and no predatory brook trout in the upper reaches. We measured baseline and stress-induced corticosterone release rates of larvae from the lower and upper reaches using a non-invasive water-borne hormone assay. We hypothesized that corticosterone release rates would differ between larvae from fish-present reaches and larvae from fish-free reaches. We found that baseline and stressor-induced corticosterone release rates were downregulated in larvae from reaches with fish predators. These results indicate that individuals from reaches with predatory trout are responding to fish predators by downregulating corticosterone while maintaining an active HPI axis. This may allow larvae more time to grow before metamorphosing, while also allowing them to physiologically respond to novel stressors. However, prolonged downregulation of corticosterone release rates can impact growth in post-metamorphic individuals.

Source–sink patch dynamics occur when movement from sources stabilizes sinks by compensating for low local vital rates. The mechanisms underlying source–sink dynamics may be complicated in species that undergo transitions between discrete life stages, particularly when stages have overlapping habitat requirements and similar movement abilities. In these species, for example, the demographic effects of movement by one stage may augment or offset the effects of movement by another stage. We used a stream salamander system to investigate patch dynamics within this form of complex life history. Specifically, we tested the hypothesis that the salamander Gyrinophilus porphyriticus experiences source–sink dynamics in riffles and pools, the dominant geomorphic patch types in headwater streams. We estimated stage‐specific survival probabilities in riffles and pools and stage‐specific movement probabilities between the two patch types using 8 years of capture–recapture data on 4491 individuals, including premetamorphic larvae and postmetamorphic adults. We then incorporated survival and movement probabilities into a stage‐structured, two‐patch model to determine the demographic interactions between riffles and pools. Monthly survival probabilities of both stages were higher in pools than in riffles. Larvae were more likely to move from riffles to pools, but adults were more likely to move from pools to riffles, despite experiencing much lower survival in riffles. In simulations, eliminating interpatch movements by both stages indicated that riffles are sinks that rely on immigration from pools for stability. Allowing only larvae to move stabilized both patch types, but allowing only adults to move destabilized pools due to the demographic cost of adult emigration. These results indicated that larval movement not only stabilizes riffles, but also offsets the destabilizing effects of maladaptive adult movement. Similar patch dynamics may emerge in any structured population in which movement and local vital rates differ by age, size, or stage. Addressing these forms of internal demographic structure in patch dynamics analyses will help to refine and advance general understanding of spatial ecology.