Restrictions on roaming Until the past century or so, the movement of wild animals was relatively unrestricted, and their travels contributed substantially to ecological processes. As humans have increasingly altered natural habitats, natural animal movements have been restricted. Tucker et al. examined GPS locations for more than 50 species. In general, animal movements were shorter in areas with high human impact, likely owing to changed behaviors and physical limitations. Besides affecting the species themselves, such changes could have wider effects by limiting the movement of nutrients and altering ecological interactions. Science , this issue p. 466

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation (normalNfalse^area) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing normalNfalse^area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small normalNfalse^area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an normalNfalse^area >1,000, where 30% had an normalNfalse^area <30. In this frequently encountered scenario of small normalNfalse^area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

Technological advances have steadily increased the detail of animal tracking datasets, yet fundamental data limitations exist for many species that cause substantial biases in home‐range estimation. Specifically, the effective sample size of a range estimate is proportional to the number of observed range crossings, not the number of sampled locations. Currently, the most accurate home‐range estimators condition on an autocorrelation model, for which the standard estimation frame‐works are based on likelihood functions, even though these methods are known to underestimate variance—and therefore ranging area—when effective sample sizes are small. Residual maximum likelihood (REML) is a widely used method for reducing bias in maximum‐likelihood (ML) variance estimation at small sample sizes. Unfortunately, we find that REML is too unstable for practical application to continuous‐time movement models. When the effective sample size N is decreased to N ≤ scriptO(10), which is common in tracking applications, REML undergoes a sudden divergence in variance estimation. To avoid this issue, while retaining REML’s first‐order bias correction, we derive a family of estimators that leverage REML to make a perturbative correction to ML. We also derive AIC values for REML and our estimators, including cases where model structures differ, which is not generally understood to be possible. Using both simulated data and GPS data from lowland tapir (Tapirus terrestris), we show how our perturbative estimators are more accurate than traditional ML and REML methods. Specifically, when scriptO(5) home‐range crossings are observed, REML is unreliable by orders of magnitude, ML home ranges are ~30% underestimated, and our perturbative estimators yield home ranges that are only ~10% underestimated. A parametric bootstrap can then reduce the ML and perturbative home‐range underestimation to ~10% and ~3%, respectively. Home‐range estimation is one of the primary reasons for collecting animal tracking data, and small effective sample sizes are a more common problem than is currently realized. The methods introduced here allow for more accurate movement‐model and home‐range estimation at small effective sample sizes, and thus fill an important role for animal movement analysis. Given REML’s widespread use, our methods may also be useful in other contexts where effective sample sizes are small.

Context Brazil has one of the richest biodiversity and one of the most extensive road networks in the world. Several negative impacts emerge from this interaction, including wildlife–vehicle collisions (WVC), which may represent a significant source of non-natural mortality in several species. The understanding of the main drivers of WVC is, therefore, crucial to improve the safe coexistence between human needs (transportation of goods and people) and animal populations. Aims We aimed to (1) evaluate the relative influence of land-cover patterns on the distribution of WVC, (2) assess whether WVCs are clustered forming hotspots of mortality, and, if so, (3) evaluate the benefits of mitigating only hotspot sections. Methods We collected WVC data involving medium–large mammals (4–260kg) along three road transects (920km), fortnightly over 1 year (n=1006 records). We used boosted regression trees to relate the WVC locations with a set of environmental variables including a roadkill index, reflecting overall habitat suitability and landscape connectivity, while accounting for spatial autocorrelation effects. We identified hotspots of mortality using Ripley’s K statistic and testing whether data follow a random Poisson distribution correcting for Type I error. Key results We found a strong association between WVC probability and roadkill index for all focal species. Distance to riparian areas, tree cover, terrain ruggedness and distance to urban areas were also important predictors, although to a lesser extent. We detected 21 hotspots of mortality, yet with little spatial overlapping as only four road sections (2%) were classified as hotspot for more than one species. Conclusions Our results supported that WVC mainly occur in road sections traversing areas with more abundant and diverse mammal communities. Hotspots of mortality may provide important information to prioritise road sections for mitigation, but this should be used in complement with roadkill indexes accounting for overall mortality. Implications The results support focusing on hotspots and habitat quality and landscape connectivity for a better assessment of road mortality. At the local scale, a larger number and improved road passages with exclusionary fencing of appropriate mesh size in riparian areas may provide safe crossings for many species and constitute a promising mitigation measure.

Data from captive animals indicated that browsing (BR) ruminants have larger fecal particles-indicative of lesser chewing efficiency-than grazers (GR). To answer whether this reflects fundamental differences between the animal groups, or different reactions of basically similar organisms to diets fed in captivity, we compared mean fecal particle size (MPS) in a GR and a BR ruminant (aurox Bos primigenius taurus, giraffe Giraffa camelopardalis) and a GR and a BR hindgut fermenter (Przewalski's horse Equus ferus przewalskii, lowland tapir Tapirus terrestris), both from captivity and from the wild. As would be expected owing to a proportion of finely ground, pelleted feeds in captive diets, MPS was smaller in captive than free-ranging GR. In contrast, MPS was drastically higher in captive than in free-ranging BR of either digestion type. Thus, the difference in MPS between GR and BR was much more pronounced among captive than free-ranging animals. The results indicate that BR teeth have adapted to their natural diet so that in the wild, they achieve a particle size reduction similar to that of GR. However, although GR teeth seem equally adapted to food ingested in captivity, the BR teeth seem less well suited to efficiently chew captive diets. In the case of ruminants, less efficient particle size reduction could contribute to potential clinical problems like "rumen blockage" and bezoar formation. Comparisons of MPS between free-ranging and captive animals might offer indications for the physical suitability of zoo diets. Zoo Biol 27:70-77, 2008. (c) 2007 Wiley-Liss, Inc.

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.

The optimisation of skeletal health during the life cycle is critical, especially if we are to reduce the continuing rise in osteoporosis -1 in 2 women and 1 in 5 men over the age of 50 years will suffer an osteoporotic fracture. The foundations of adult bone health are laid down in the early years; therefore, optimisation of bone health in the young is fundamental. Although genetics play a major role, accounting for 70-75% of bone strength, other lifestyle and nutrition factors are known to be highly influential. Calcium (Ca) and vitamin D play critical roles in bone mineralisation as well as generally being key nutrients in health. All living cells require Ca to survive, with the majority (99%) of Ca being found in bones and teeth and the remainder in soft tissues and body fluids. Vitamin D is the generic term for two molecules: ergocalciferol (vitamin D2) and cholecalciferol (vitamin D3). The former is derived by ultraviolet (UV) irradiation of ergosterol, which is distributed in plants and fungi. The latter is formed from the effect of UV irradiation on the skin. The principal role of vitamin D is to support the serum Ca concentration within narrow limits. Vitamin D is crucial for maximising gut absorption of calcium via vitamin D dependent Ca receptors. It is estimated that adequate vitamin D status increases Ca absorption to 30-40% of intake compared with only 10-15% absorption without adequate vitamin D. Intakes of Ca are a concern among certain groups of the population, for example a high proportion (>12%) of teenage boys and girls fail to meet the lower reference nutrient intake for Ca. For vitamin D, there are no dietary reference values for the age group 4-64 years as it is considered that UV exposure provides sufficient quantities of vitamin D, but there is now mounting evidence of widespread vitamin D insufficiency in the population. Weight-bearing physical activity is beneficial to the skeleton but clarification is needed of the exact type, intensity and duration required for optimal bone mass. The role of othermicronutrients on bone metabolism remains to be fully quantified. This review investigates the current evidence of the impact of dietary and lifestyle factors on bone health, with specific reference to children and adolescents and with a focus on vitamin D, Ca and weight-bearing exercise. © 2007 The Authors; Journal compilation © 2007 British Nutrition Foundation

A population viability analysis (PVA) was conducted of the lowland tapir populations in the Atlantic Forest of the Pontal do Paranapanema region, Brazil, including Morro do Diabo State Park (MDSP) and surrounding forest fragments. Results from the model projected that the population of 126 tapirs in MDSP is likely to persist over the next 100 years; however, 200 tapirs would be required to maintain a viable population. Sensitivity analysis showed that sub-adult mortality and adult mortality have the strongest influence on the dynamics of lowland tapir populations. High road-kill has a major impact on the MDSP tapir population and can lead to population extinction. Metapopulation modeling showed that dispersal of tapirs from MDSP to the surrounding fragments can be detrimental to the overall metapopulation, as fragments act as sinks. Nevertheless, the model showed that under certain conditions the maintenance of the metapopulation dynamics might be determinant for the persistence of tapirs in the region, particularly in the smaller fragments. The establishment of corridors connecting MDSP to the forest fragments models resulted in an increase in the stochastic growth rate, making tapirs more resilient to threats and catastrophes, but only if rates of mortality were not increased when using corridors. The PVA showed that the conservation of tapirs in the Pontal region depends on: the effective protection of MDSP; maintenance and, whenever possible, enhancement of the functional connectivity of the landscape, reducing mortality during dispersal and threats in the unprotected forest fragments; and neutralization of all threats affecting tapirs in the smaller forest fragments.