Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles.

BACKGROUND: Animals of many different species, trophic levels, and life history strategies migrate, and the improvement of animal tracking technology allows ecologists to collect increasing amounts of detailed data on these movements. Understanding when animals migrate is important for managing their populations, but is still difficult despite modelling advancements. METHODS: We designed a model that parametrically estimates the timing of migration from animal tracking data. Our model identifies the beginning and end of migratory movements as signaled by change-points in step length and turning angle distributions. To this end, we can also use the model to estimate how an animal's movement changes when it begins migrating. In addition to a thorough simulation analysis, we tested our model on three datasets: migratory ferruginous hawks (Buteo regalis) in the Great Plains, barren-ground caribou (Rangifer tarandus groenlandicus) in northern Canada, and non-migratory brown bears (Ursus arctos) from the Canadian Arctic. RESULTS: Our simulation analysis suggests that our model is most useful for datasets where an increase in movement speed or directional autocorrelation is clearly detectable. We estimated the beginning and end of migration in caribou and hawks to the nearest day, while confirming a lack of migratory behaviour in the brown bears. In addition to estimating when caribou and ferruginous hawks migrated, our model also identified differences in how they migrated; ferruginous hawks achieved efficient migrations by drastically increasing their movement rates while caribou migration was achieved through significant increases in directional persistence. CONCLUSIONS: Our approach is applicable to many animal movement studies and includes parameters that can facilitate comparison between different species or datasets. We hope that rigorous assessment of migration metrics will aid understanding of both how and why animals move.

Association between high-sensitivity cardiac troponin I and fall-related hospitalisation in women aged over 70 years.

OBJECTIVE: To examine the association between high-sensitivity cardiac troponin I (hs-cTnI), a biomarker of myocardial injury, muscle function decline and 14.5-year fall-related hospitalisation risk in women aged over 70 years. METHODS: 1179 ambulatory community-dwelling women aged over 70 years with subclinical levels of hs-cTnI (ie, <15.6 ng/L), who were followed up for 14.5 years, were included. Samples for hs-cTnI were obtained in 1998. Fall-related hospitalisations were retrieved from linked health records. Muscle function measures, including handgrip strength and the Timed-Up-and-Go (TUG) test, were assessed in 1998 and 2003. RESULTS: Mean±SD age was 75.2±2.7 years. Over 14.5 years of follow-up, 40.4% (476 of 1179) experienced fall-related hospitalisation. Participants were categorised into four approximate hs-cTnI quartiles: quartile 1 (<3.6 ng/L), quartile 2 (3.6-4.4 ng/L), quartile 3 (4.5-5.8 ng/L) and quartile 4 (≥5.9 ng/L). Compared with those in Q1, women in Q4 were likely to experience fall-related hospitalisation (36.0% vs 42.8%). In a multivariable-adjusted model that accounted for CVD and fall risk factors, compared with women in Q1, those in Q4 had a 46% higher risk of fall-related hospitalisation (HR 1.46, 95% CI 1.08 to 1.98). Additionally, women in Q4 had slower TUG performance compared with those in Q1 (10.3 s vs 9.5 s, p=0.032). CONCLUSION: Elevated level of hs-cTnI was associated with slower TUG performance and increased fall-related hospitalisation risk. This indicates subclinical level of hs-cTnI can identify clinically relevant falls, emphasising the need to consider cardiac health during fall assessment in women aged over 70 years. TRIAL REGISTRATION NUMBER: ACTRN12617000640303.

Time-dependent memory and individual variation in Arctic brown bears (Ursus arctos).

BACKGROUND: Animal movement modelling provides unique insight about how animals perceive their landscape and how this perception may influence space use. When coupled with data describing an animal's environment, ecologists can fit statistical models to location data to describe how spatial memory informs movement. METHODS: We performed such an analysis on a population of brown bears (Ursus arctos) in the Canadian Arctic using a model incorporating time-dependent spatial memory patterns. Brown bear populations in the Arctic lie on the periphery of the species' range, and as a result endure harsh environmental conditions. In this kind of environment, effective use of memory to inform movement strategies could spell the difference between survival and mortality. RESULTS: The model we fit tests four alternate hypotheses (some incorporating memory; some not) against each other, and we found a high degree of individual variation in how brown bears used memory. We found that 71% (15 of 21) of the bears used complex, time-dependent spatial memory to inform their movement decisions. CONCLUSIONS: These results, coupled with existing knowledge on individual variation in the population, highlight the diversity of foraging strategies for Arctic brown bears while also displaying the inference that can be drawn from this innovative movement model.

No effect of passive integrated transponder tagging method on survival or body condition in a northern population of Black-capped Chickadees (Poecile atricapillus).

Passive integrated transponder (PIT) tags allow a range of individual-level data to be collected passively and have become a commonly used technology in many avian studies. Although the potential adverse effects of PIT tags have been evaluated in several species, explicit investigations of their impacts on small (<12 g) birds are limited. This is important, because it is reasonable to expect that smaller birds could be impacted more strongly by application of PIT tags. In this study, we individually marked Black-capped Chickadees (Poecile atricapillus), a small (circa 10 g) passerine, at the University of Alberta Botanic Garden to evaluate potential lethal and sublethal effects of two PIT tagging methods: attachment to leg bands or subcutaneous implantation. We used a Cox proportional hazards model to compare the apparent survival of chickadees with leg band (N = 79) and implanted PIT tags (N = 77) compared with control birds that received no PIT tags (N = 76) over the subsequent 2 years based on mist net recaptures. We used radio-frequency identification (RFID) redetections of leg band PIT tags to evaluate sex-specific survival and increase the accuracy of our survival estimates. We also used a generalized linear regression model to compare the body condition of birds recaptured after overwintering with leg band PIT tags, implanted PIT tags, or neither. Our analysis found no evidence for adverse effects of either PIT tagging method on survival or body condition. While we recommend carefully monitoring study animals and evaluating the efficacy of different PIT tagging methods, we have shown that both leg band and subcutaneously implanted PIT tags ethical means of obtaining individualized information in a small passerine.

Predictor species: Improving assessments of rare species occurrence by modeling environmental co-responses.

Designing an effective conservation strategy requires understanding where rare species are located. Because rare species can be difficult to find, ecologists often identify other species called conservation surrogates that can help inform the distribution of rare species. Species distribution models typically rely on environmental data when predicting the occurrence of species, neglecting the effect of species' co-occurrences and biotic interactions. Here, we present a new approach that uses Bayesian networks to improve predictions by modeling environmental co-responses among species. For species from a European peat bog community, our approach consistently performs better than single-species models and better than conventional multi-species approaches that include the presence of nontarget species as additional independent variables in regression models. Our approach performs particularly well with rare species and when calibration data are limited. Furthermore, we identify a group of "predictor species" that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices.