Implications of ignoring telemetry error on inference in wildlife resource use models

Global Positioning System (GPS) and very high frequency (VHF) telemetry data redefined the examination of wildlife resource use. Researchers collar animals, relocate those animals over time, and utilize the estimated locations to infer resource use and build predictive models. Precision of these estimated wildlife locations, however, influences the reliability of point-based models with accuracy depending on the interaction between mean telemetry error and how habitat characteristics are mapped (categorical raster resolution and patch size). Telemetry data often foster the assumption that locational error can be ignored without biasing study results. We evaluated the effects of mean telemetry error and categorical raster resolution on the correct characterization of patch use when locational error is ignored. We found that our ability to accurately attribute patch type to an estimated telemetry location improved nonlinearly as patch size increased and mean telemetry error decreased. Furthermore, the exact shape of these relationships was directly influenced by categorical raster resolution. Accuracy ranged from 100% (200-ha patch size, 1- to 5-m telemetry error) to 46% (0.5-ha patch size, 56- to 60-m telemetry error) for 10 m resolution rasters. Accuracy ranged from 99% (200-ha patch size, 1- to 5-m telemetry error) to 57% (0.5-ha patch size, 56- to 60-m telemetry error) for 30-m resolution rasters. When covariate rasters were less resolute (30 m vs. 10 m) estimates for the ignore technique were more accurate at smaller patch sizes. Hence, both fine resolution (10 m) covariate rasters and small patch sizes increased probability of patch misidentification. Our results help frame the scope of ecological inference made from point-based wildlife resource use models. For instance, to make ecological inferences with 90% accuracy at small patch sizes (≤5 ha) mean telemetry error ≤5 m is required for 10-m resolution categorical rasters. To achieve the same inference on 30-m resolution categorical rasters, mean telemetry error ≤10 m is required. We encourage wildlife professionals creating point-based models to assess whether reasonable estimates of resource use can be expected given their telemetry error, covariate raster resolution, and range of patch sizes. © 2011 The Wildlife Society.     

Can We Accurately Characterize Wildlife Resource Use When Telemetry Data Are Imprecise?

ABSTRACT Telemetry data have been widely used to quantify wildlife habitat relationships despite the fact that these data are inherently imprecise. All telemetry data have positional error, and failure to account for that error can lead to incorrect predictions of wildlife resource use. Several techniques have been used to account for positional error in wildlife studies. These techniques have been described in the literature, but their ability to accurately characterize wildlife resource use has never been tested. We evaluated the performance of techniques commonly used for incorporating telemetry error into studies of wildlife resource use. Our evaluation was based on imprecise telemetry data (mean telemetry error = 174 m, SD = 130 m) typical of field-based studies. We tested 5 techniques in 10 virtual environments and in one real-world environment for categorical (i.e., habitat types) and continuous (i.e., distances or elevations) rasters. Technique accuracy varied by patch size for the categorical rasters, with higher accuracy as patch size increased. At the smallest patch size (1 ha), the technique that ignores error performed best on categorical data (0.31 and 0.30 accuracy for virtual and real data, respectively); however, as patch size increased the bivariate-weighted technique performed better (0.56 accuracy at patch sizes >31 ha) and achieved complete accuracy (i.e., 1.00 accuracy) at smaller patch sizes (472 ha and 1,522 ha for virtual and real data, respectively) than any other technique. We quantified the accuracy of the continuous covariates using the mean absolute difference (MAD) in covariate value between true and estimated locations. We found that average MAD varied between 104 m (ignore telemetry error) and 140 m (rescale the covariate data) for our continuous covariate surfaces across virtual and real data sets. Techniques that rescale continuous covariate data or use a zonal mean on values within a telemetry error polygon were significantly less accurate than other techniques. Although the technique that ignored telemetry error performed best on categorical rasters with smaller average patch sizes (i.e., ≤31 ha) and on continuous rasters in our study, accuracy was so low that the utility of using point-based approaches for quantifying resource use is questionable when telemetry data are imprecise, particularly for small-patch habitat relationships.     

Below-threshold mortality: implications for studies in evolution,ecology and demography

Evolutionary biologists, ecologists and experimental gerontologists have increasingly used estimates of age-specific mortality as a critical component in studies of a range of important biological processes. However, the analysis of age-specific mortality rates is plagued by specific statistical challenges caused by sampling error. Here we discuss the nature of this ‘demographic sampling error’, and the way in which it can bias our estimates of (1) rates of ageing, (2) age at onset of senescence, (3) costs of reproduction and (4) demographic tests of evolutionary models of ageing. We conducted simulations which suggest that using standard statistical techniques, we would need sample sizes on the order of tens of thousands in most experiments to effectively remove any bias due to sampling error. We argue that biologists should use much larger sample sizes than have previously been used. However, we also present simple maximum likelihood models that effectively remove biases due to demographic sampling error even at relatively small sample sizes.     

Evaluation of Root-n Bandwidth Selectors for Kernel Density Estimation

ABSTRACT The kernel density estimator is used commonly for estimating animal utilization distributions from location data. This technique requires estimation of a bandwidth, for which ecologists often use least-squares cross-validation (LSCV). However, LSCV has large variance and a tendency to under-smooth data, and it fails to generate a bandwidth estimate in some situations. We compared performance of 2 new bandwidth estimators (root-n) versus that of LSCV using simulated data and location data from sharp-shinned hawks (Accipter striatus) and red wolves (Canis rufus). With simulated data containing no repeat locations, LSCV often produced a better fit between estimated and true utilization distributions than did root-n estimators on a case-by-case basis. On average, LSCV also provided lower positive relative error in home-range areas with small sample sizes of simulated data. However, root-n estimators tended to produce a better fit than LSCV on average because of extremely poor estimates generated on occasion by LSCV. Furthermore, the relative performance of LSCV decreased substantially as the number of repeat locations in the data increased. Root-n estimators also generally provided a better fit between utilization distributions generated from subsamples of hawk data and the local densities of locations from the full data sets. Least-squares cross-validation generated more unrealistically disjointed estimates of home ranges using real location data from red wolf packs. Most importantly, LSCV failed to generate home-range estimates for >20% of red wolf packs due to presence of repeat locations. We conclude that root-n estimators are superior to LSCV for larger data sets with repeat locations or other extreme clumping of data. In contrast, LSCV may be superior where the primary interest is in generating animal home ranges (rather than the utilization distribution) and data sets are small with limited clumping of locations.     

Enhancing the use of Argos satellite data for home range and long distance migration studies of marine animals

Accurately quantifying animals' spatial utilisation is critical for conservation, but has long remained an elusive goal due to technological impediments. The Argos telemetry system has been extensively used to remotely track marine animals, however location estimates are characterised by substantial spatial error. State-space models (SSM) constitute a robust statistical approach to refine Argos tracking data by accounting for observation errors and stochasticity in animal movement. Despite their wide use in ecology, few studies have thoroughly quantified the error associated with SSM predicted locations and no research has assessed their validity for describing animal movement behaviour. We compared home ranges and migratory pathways of seven hawksbill sea turtles (Eretmochelys imbricata) estimated from (a) highly accurate Fastloc GPS data and (b) locations computed using common Argos data analytical approaches. Argos 68(th) percentile error was <1 km for LC 1, 2, and 3 while markedly less accurate (>4 km) for LC ≤ 0. Argos error structure was highly longitudinally skewed and was, for all LC, adequately modelled by a Student's t distribution. Both habitat use and migration routes were best recreated using SSM locations post-processed by re-adding good Argos positions (LC 1, 2 and 3) and filtering terrestrial points (mean distance to migratory tracks ± SD = 2.2 ± 2.4 km; mean home range overlap and error ratio = 92.2% and 285.6 respectively). This parsimonious and objective statistical procedure however still markedly overestimated true home range sizes, especially for animals exhibiting restricted movements. Post-processing SSM locations nonetheless constitutes the best analytical technique for remotely sensed Argos tracking data and we therefore recommend using this approach to rework historical Argos datasets for better estimation of animal spatial utilisation for research and evidence-based conservation purposes.     

Patterns of Spatial use and movement of the Poóuli – a critically endangered Hawaiian honeycreeper

The Poóuli (Melamprosops phaeosoma) is a highly endangered Hawaiian honeycreeper endemic to Maui, and is currently one of the World's rarest birds. Only two wild individuals of this species are now known to exist, and they are restricted to the windward slopes of Haleakala volcano on east Maui. Studies of the more common honeycreepers on the Hawaiian islands describe a diverse array of spatial use and movement patterns, which vary according to specific ecological needs. In contrast, spatial use and movement of Poóuli are very poorly known, despite continuous field monitoring of all three individuals since 1997. We analyzed annual data by breeding season between 1995 and 2001 for three individuals, and eight days of telemetry data derived from the radio-tracking of one individual in 2002, using GIS and conventional methods, to estimate home range size, and to interpret these data alongside those for other honeycreepers. We estimated mean home range sizes of 8.43 hectares (ha) from annual re-sights using kernel analysis and 2.14 ha using minimum convex polygons, and 8.44 and 3.51 ha respectively from telemetry data. Our estimates conform to those derived for other insectivorous honeycreepers, but indicate that Poóuli may forage widely to support their diet of forest snails. Our home range size estimates are compatible with estimates of population density for Poóuli that were derived from field transects between 1975 and 1985, suggesting that such field methods may be a reliable density estimator for rare forest birds. Jim J. Groombridge and Bill Sparklin: Joint first author     

Evaluating distributional shifts in home range estimates

A variety of methods are commonly used to quantify animal home ranges using location data acquired with telemetry. High‐volume location data from global positioning system (GPS) technology provide researchers the opportunity to identify various intensities of use within home ranges, typically quantified through utilization distributions (UDs). However, the wide range of variability evident within UDs constructed with modern home range estimators is often overlooked or ignored during home range comparisons, and challenges may arise when summarizing distributional shifts among multiple UDs. We describe an approach to gain additional insight into home range changes by comparing UDs across isopleths and summarizing comparisons into meaningful results. To demonstrate the efficacy of this approach, we used GPS location data from 16 bighorn sheep (Ovis canadensis) to identify distributional changes before and after habitat alterations, and we discuss advantages in its application when comparing home range size, overlap, and joint‐space use. We found a consistent increase in bighorn sheep home range size when measured across home range levels, but that home range overlap and similarity values decreased when examined at increasing core levels. Our results highlight the benefit of conducting multiscale assessments when comparing distributions, and we encourage researchers to expand comparative home range analyses to gain a more comprehensive evaluation of distributional changes and to evaluate comparisons across home range levels.     

Field test of an automated radio‐telemetry system: tracking local space use of aerial insectivores

Documenting local space use of birds that move rapidly, but are too small to carry GPS tags, such as swallows and swifts, can be challenging. For these species, tracking methods such as manual radio‐telemetry and visual observation are either inadequate or labor‐ and time‐intensive. Another option is use of an automated telemetry system, but equipment for such systems can be costly when many receivers are used. Our objective, therefore, was to determine if an automated radio‐telemetry system, consisting of just two receivers, could provide an alternative to manual tracking for gathering data on local space use of six individuals of three species of aerial insectivores, including one Cliff Swallow (Petrochelidon pyrrhonota), one Eastern Phoebe (Sayornis phoebe), and four Barn Swallows (Hirundo rustica). We established automated radio‐telemetry systems at three sites near the city of Peterborough in eastern Ontario, Canada, from May to August 2015. We evaluated the location error of our two‐receiver system using data from moving and stationary test transmitters at known locations, and used telemetry data from the aerial insectivores as a test of the system's ability to track rapidly moving birds under field conditions. Median location error was ~250 m for automated telemetry test locations after filtering. More than 90% of estimated locations had large location errors and were removed from analysis, including all locations > 1 km from receiver stations. Our automated telemetry receivers recorded 17,634 detections of the six radio‐tagged birds. However, filtering removed an average of 89% of bird location estimates, leaving only the Cliff Swallow with enough locations for analysis of space use. Our results demonstrate that a minimal automated radio‐telemetry system can be used to assess local space use by small, highly mobile birds, but the resolution of the data collected using only two receiver stations was coarse and had a limited range. To improve both location accuracy and increase the percentage of usable location estimates collected, we suggest that, in future studies, investigators use receivers that simultaneously record signals detected by all antennas, and use of a minimum of three receiver stations with more antennas at each station.     

Effects of sampling regime on the mean and variance of home range size estimates

1. Although the home range is a fundamental ecological concept, there is considerable debate over how it is best measured. There is a substantial literature concerning the precision and accuracy of all commonly used home range estimation methods; however, there has been considerably less work concerning how estimates vary with sampling regime, and how this affects statistical inferences. 2. We propose a new procedure, based on a variance components analysis using generalized mixed effects models to examine how estimates vary with sampling regime. 3. To demonstrate the method we analyse data from one study of 32 individually marked roe deer and another study of 21 individually marked kestrels. We subsampled these data to simulate increasingly less intense sampling regimes, and compared the performance of two kernel density estimation (KDE) methods, of the minimum convex polygon (MCP) and of the bivariate ellipse methods. 4. Variation between individuals and study areas contributed most to the total variance in home range size. Contrary to recent concerns over reliability, both KDE methods were remarkably efficient, robust and unbiased: 10 fixes per month, if collected over a standardized number of days, were sufficient for accurate estimates of home range size. However, the commonly used 95% isopleth should be avoided; we recommend using isopleths between 90 and 50%. 5. Using the same number of fixes does not guarantee unbiased home range estimates: statistical inferences differ with the number of days sampled, even if using KDE methods. 6. The MCP method was highly inefficient and results were subject to considerable and unpredictable biases. The bivariate ellipse was not the most reliable method at low sample sizes. 7. We conclude that effort should be directed at marking more individuals monitored over long periods at the expense of the sampling rate per individual. Statistical results are reliable only if the whole sampling regime is standardized. We derive practical guidelines for field studies and data analysis.     

Effects of Point Pattern Shape on Home-Range Estimates

Abstract: Home-range estimators are commonly tested with simulated animal locational data in the laboratory before the estimators are used in practice. Although kernel density estimation (KDE) has performed well as a home-range estimator for simulated data, several recent studies have reported its poor performance when used with data collected in the field. This difference may be because KDE and other home-range estimators are generally tested with simulated point locations that follow known statistical distributions, such as bivariate normal mixtures, which may not represent well the space-use patterns of all wildlife species. We used simulated animal locational data of 5 point pattern shapes that represent a range of wildlife utilization distributions to test 4 methods of home-range estimation: 1) KDE with reference bandwidths, 2) KDE with least-squares cross-validation, 3) KDE with plug-in bandwidths, and 4) minimum convex polygon (MCP). For the point patterns we simulated, MCP tended to produce more accurate area estimates than KDE methods. However, MCP estimates were markedly unstable, with bias varying widely with both sample size and point pattern shape. The KDE methods performed best for concave distributions, which are similar to bivariate normal mixtures, but still overestimated home ranges by about 40–50% even in the best cases. For convex, linear, perforated, and disjoint point patterns, KDE methods overestimated home-range sizes by 50–300%, depending on sample size and method of bandwidth selection. These results indicate that KDE does not produce home-range estimates that are as accurate as the literature suggests, and we recommend exploring other techniques of home-range estimation.     

A comparative analysis of alternative approaches to fitting species-abundance models

Aims Fits of species-abundance distributions to empirical data are increasingly used to evaluate models of diversity maintenance and community structure and to infer properties of communities, such as species richness. Two distributions predicted by several models are the Poisson lognormal (PLN) and the negative binomial (NB) distribution; however, at least three different ways to parameterize the PLN have been proposed, which differ in whether unobserved species contribute to the likelihood and in whether the likelihood is conditional upon the total number of individuals in the sample. Each of these has an analogue for the NB. Here, we propose a new formulation of the PLN and NB that includes the number of unobserved species as one of the estimated parameters. We investigate the performance of parameter estimates obtained from this reformulation, as well as the existing alternatives, for drawing inferences about the shape of species abundance distributions and estimation of species richness.Methods We simulate the random sampling of a fixed number of individuals from lognormal and gamma community relative abundance distributions, using a previously developed 'individual-based' bootstrap algorithm. We use a range of sample sizes, community species richness levels and shape parameters for the species abundance distributions that span much of the realistic range for empirical data, generating 1?000 simulated data sets for each parameter combination. We then fit each of the alternative likelihoods to each of the simulated data sets, and we assess the bias, sampling variance and estimation error for each method.Important findings Parameter estimates behave reasonably well for most parameter values, exhibiting modest levels of median error. However, for the NB, median error becomes extremely large as the NB approaches either of two limiting cases. For both the NB and PLN,>90% of the variation in the error in model parameters across parameter sets is explained by three quantities that corresponded to the proportion of species not observed in the sample, the expected number of species observed in the sample and the discrepancy between the true NB or PLN distribution and a Poisson distribution with the same mean. There are relatively few systematic differences between the four alternative likelihoods. In particular, failing to condition the likelihood on the total sample sizes does not appear to systematically increase the bias in parameter estimates. Indeed, overall, the classical likelihood performs slightly better than the alternatives. However, our reparameterized likelihood, for which species richness is a fitted parameter, has important advantages over existing approaches for estimating species richness from fitted species-abundance models.     

Estimation of the maximum utilization area including home range and peripheral sites

There is increasing evidence that occasional utilization area (peripheral sites), in addition to typical utilization area (home range), is important for wildlife conservation and management. Here we estimated the maximum utilization area (MUA), including both typical and occasional utilization areas, based on asymptotic curves of utilization area plotted against sample size. In previous studies, these curves have conventionally been plots of cumulative utilization area versus sample size, but this cumulative method is sensitive to stochastic effects. We propose a new method based on simulation studies where outcomes of replicated simulations are averaged to reduce stochastic effects. In this averaged method, possible combinations of sample size with the same number of location data replicated from a dataset were averaged and applied to the curves of utilization area. The cumulative method resulted in a large variation of MUA estimates, depending on the start date as well as total sample size of the dataset. In the averaged method, MUA estimates were robust against changes in the start date and total sample size. The large variation of MUA estimates arose because location data on any day including the start date are affected by unpredictable effects associated with animal activity and environmental conditions. In the averaged method, replicates of sample size resulted in a reduction of temporal stochasticity, suggesting that the method stably provides reliable estimates for MUA.     

Autocorrelated data in telemetry studies: time to independence and the problem of behavioural effects

Independence of locational fixes, to reduce the effects of autocorrelation, is often deemed a prerequisite for estimation of home range size and utilization when using data derived from telemetric studies. Three methods of estimating times to independence using movement estimates, along with a statistical method of estimating the level of autocorrelation of locational data, were examined for two species of mammal. Attempts to achieve statistically independent data by subsampling resulted in severe redundancy in the data and significant underestimation of range size and rates of movement. Even a sample interval of one fix per week did not guarantee independence and also resulted in underestimation of range size despite range asymptotes being reached. It would appear that the correct strategy for the best possible estimation of range size and use from telemetry would be the repeated use of as short a sampling interval as is possible over an extended period of time. Statistical methods to measure levels of autocorrelation in locational data may be useful for comparing rates of range use between different populations of the same species or between species, as long as the same sample interval is used.     

Home range of the European rabbit (Oryctolagus cuniculus) in three contrasting French populations

Despite their pest status in numerous areas throughout the World, the populations of European rabbits (Oryctolagus cuniculus) have strongly decreased in South Western Europe since the mid-20th century. Such a decrease constitutes a major threat on top predators and calls for a better understanding of its mechanisms to provide suitable management responses. Infectious diseases have been invoked as the main responsible factors, but they cannot by themselves explain the magnitude of this decrease. Habitat fragmentation may indeed act as a synergetic factor, and habitat use studies are needed to better understand the impact of fragmentation on rabbit population dynamics. We investigated the variability of home range size with respects to age, sex and season in three wild populations of rabbits using telemetry. Home ranges were smaller in the highest density populations (7333 and 6878 vs. 20,492 m²) suggesting differences in habitat quality between the populations. In addition, home range sizes were larger during the reproductive season for both sexes, and adults tended to have smaller home ranges than juveniles. Clearly, the home range sizes reported here were smaller than those previously reported in rabbits.     

Assessing the accuracy of small satellite transmitters on free‐living flying‐foxes

Satellite telemetry using ARGOS platform transmitter terminals (PTTs) is widely used to track the movements of animals, but little is known of the accuracy of these systems when used on active terrestrial mammals. An accurate estimate of the error, and therefore the limitations of the data, is critical when assessing the level of confidence in results. ARGOS provides published 68th percentile error estimates for the three most accurate location classes (LCs), but studies have shown that the errors can be far greater when the devices are attached to free‐living animals. Here we use data from a study looking at the habitat use of the spectacled flying‐fox in the wet tropics of Queensland to calculate these errors for all LCs in free‐living terrestrial mammals, and use these results to assess what level of confidence we would have in habitat use assignment in the study area. The results showed that our calculated 68th percentile errors were larger than the published ARGOS errors for all LCs, and that for all classes the error frequency had a very long tail. Habitat use results showed that the size of the error compared with the scale of the habitat the study was conducted in makes it unlikely that our data can be used to assess habitat use with great confidence. Overall, our results show that while satellite telemetry results are useful for assessing large scale movements of animals, in complex landscapes they may not be accurate enough to be used for finer scale analysis including habitat use assessment.     

An Integrated Vehicle-Mounted Telemetry System for VHF Telemetry Applications

Abstract: We designed and developed a vehicle-mounted very high frequency-based telemetry system that integrated an on-board antenna, receiver, electronic compass, Global Positioning System, computer, and Geographic Information System. The system allows users to accurately and quickly obtain fixes, estimate and confirm locations of radiomarked animals, and immediately record data into an electronic spreadsheet or database. The total cost of materials to build the system was $7,349 (United States currency). Mean error angle of 2.63 ± 12.1° (SD; range = −33.7–42.2°) and mean location error distance of 128 ± 91.3 m (SD; range = 0–408 m) suggested precision and accuracy of our system were comparable to other reported systems. Mean time to record 5 bearings/test transmitter was 6.28 ± 0.24 minutes (SE), which is the most efficient system reported to locate animals in the field. Vehicle-mounted telemetry systems like ours provide additional value to studies that involve tracking highly mobile species because investigators need not take bearings from established receiving stations and because investigators can immediately recognize bounced signals and take additional bearings and optimize accuracy of location estimates.     

Estimating utilization distributions with kernel versus local convex hull methods

Estimates of utilization distributions (UDs) are used in analyses of home-range area, habitat and resource selection, and social interactions. We simulated data from 12 parent UDs, representing 3 series of increasingly intense space-use patterns (clustering of points around a home site, restriction of locations to a network of nodes and corridors, and dominance of a central hole in the UD) and compared the ability of kernel density estimation (KDE) and local convex hull (LCH) construction to reconstruct known UDs from samples of 10, 50, 250, and 1,000 location points. For KDE, we considered 4 bandwidth selectors: the reference bandwidth, least-squares cross-validation (LSCV), direct plug-in (DPI), and solve-the-equation (STE). For the sample sizes and UD patterns tested here, KDE achieved significantly higher volume-of-intersection (VI) scores with known parent UDs than did LCH; KDE also provided less biased home-range area estimates under many conditions. However, LCH minimized the UD volume that occurred outside the true home range boundary (V_out). Among the KDE bandwidth estimators, relative performance depended on the type and intensity of space use patterns, sample size, and the metric used to evaluate performance. Biologists should use KDE for UD and home range estimation within a probabilistic context, unless their objective is to exclude potentially unused areas by defining the area delimited by data. © 2011 The Wildlife Society.     

Population densities and home range sizes of the Chestnut Wood-quail

ABSTRACT.   We present the first report of population density and home range estimates for the Chestnut Wood-quail ( Odontophorus hyperythrus ), an endemic and nearly threatened species of the Colombian Andes. Density estimates were obtained using playbacks and spot mapping and point transect methods. Home range sizes were estimated by radio tracking five wood-quail. Estimated densities varied from 0.3 to 0.4 groups per hectare. These densities are similar to those reported for other mountain wood-quail, but higher than those for lowland wood-quail and other species in the family Odontophoridae. Estimates of home range size varied from 2.6 to 9.0 hectares per group, and seemed to be correlated with group size. We also found evidence of overlap in the home ranges of neighboring groups. Population densities may vary with factors such as habitat type, but our estimates allow a first approximation of the population sizes of Chestnut Wood-quail in forest fragments.     

The effect of intraspecific sample size on type I and type II error rates in comparative studies

Comparative studies have increased greatly in number in recent years due to advances in statistical and phylogenetic methodologies. For these studies, a trade-off often exists between the number of species that can be included in any given study and the number of individuals examined per species. Here, we describe a simple simulation study examining the effect of intraspecific sample size on statistical error in comparative studies. We find that ignoring measurement error has no effect on type I error of nonphylogenetic analyses, but can lead to increased type I error under some circumstances when using independent contrasts. We suggest using ANOVA to evaluate the relative amounts of within- and between-species variation when considering a phylogenetic comparative study. If within-species variance is particularly large and intraspecific sample sizes small, then either larger sample sizes or comparative methods that account for measurement error are necessary.     

Bachman's Sparrows at the northern periphery of their range: home range size and microhabitat selection

Populations of Bachman's Sparrows (Peucaea aestivalis) have declined range‐wide since the late 1960s. Populations at the periphery of their range have exhibited some of the steepest declines, and these sparrows are now rare or extirpated over much of the northern extent of their historical range. To better understand the spatial ecology of Bachman's Sparrows in this region of decline, we examined microhabitat selection and determined the home range sizes of radio‐tagged male Bachman's Sparrows (N = 37) in the Coastal Plain of North Carolina in 2014 and 2015. From April to July, we located males 1–2 times daily for 5–6 d per week. We measured vegetation structure in home ranges using 5‐m‐radius plots centered on a subset of 10 randomly selected telemetry locations as well as in available unused locations 50 m and in a random direction from each telemetry location. Mean size of home ranges (7.9 ha) was larger than estimates reported in most previous studies, with differences among studies possibly due, at least in part, to differences in the characteristics of habitats where studies were conducted. The home ranges of Bachman's Sparrows in our study had greater densities of woody and dead vegetation than unused areas. Although generally considered detrimental to the presence of Bachman's Sparrows, the presence of some woody vegetation in frequently burned (i.e., ≤ 3‐yr return interval) longleaf pine (Pinus palustris) communities like those in our study may be important in providing song perches for males and cover from attacking predators. Bachman's Sparrows in our study showed clear selection for several vegetation characteristics linked to frequent fire. Management strategies that approximate historical fire regimes in longleaf pine ecosystems should continue to be promoted as essential tools for the conservation of Bachman's Sparrows.