Methods for Locating African Lion Kills Using Global Positioning System Movement Data

ABSTRACT Knowledge of the range, behavior, and feeding habits of large carnivores is fundamental to their successful conservation. Traditionally, the best method to obtain feeding data is through continuous observation, which is not always feasible. Reliable automated methods are needed to obtain sample sizes sufficient for statistical inference. Identification of large carnivore kill sites using Global Positioning System (GPS) data is gaining popularity. We assessed performance of generalized linear regression models (GLM) versus classification trees (CT) in a multipredator, multiprey African savanna ecosystem. We applied GLMs and CTs to various combinations of distance-traveled data, cluster durations, and environmental factors to predict occurrence of 234 female African lion (Panthera leo) kill sites from 1,477 investigated GPS clusters. Ratio of distance moved 24 hours before versus 24 hours after a cluster was the most important predictor variable in both GLM and CT analysis. In all cases, GLMs outperformed our cost-complexity-pruned CTs in their discriminative ability to separate kill from nonkill sites. Generalized linear models provided a good framework for kill-site identification that incorporates a hierarchal ordering of cluster investigation and measures to assess trade-offs between classification accuracy and time constraints. Implementation of GLMs within an adaptive sampling framework can considerably increase efficiency of locating kill sites, providing a cost-effective method for increasing sample sizes of kill data.     

Discriminating grey wolf kill sites using GPS clusters

Characteristics of spatio-temporal clusters of locations from global positioning system (GPS)-collars have been used to distinguish kill sites of various predators. We deployed GPS collars on 9 grey wolves (Canis lupus) in the southwest area of Prince Albert National Park in central Saskatchewan, Canada, and used a GPS location clustering algorithm to identify kill sites of ungulate and other large-bodied prey during winter, December 2013–March 2017. We used logistic regression in a model-selection framework to determine if spatio-temporal and habitat characteristics of grey wolf GPS clusters could be used to reliably identify sites where wolves had killed prey. Global positioning system clusters were more likely to be wolf kill sites when they had a higher number of location fixes, did not begin within 300 m and 30 days of a previous cluster, did not begin within 1 km and 4 days of a previous cluster, began in the evening, had a high percentage of fixes occurring during the day, occurred farther from open habitat, and had both a high number of location fixes and a high percentage of fixes occurring during the day. Our results highlight the limits of using spatio-temporal clusters with a fix rate of 1/hour to discriminate wolf kill sites in systems dominated by deer (Odocoileus spp.) because of the associated short handling time with these prey.     

Evaluating Global Positioning System Telemetry Techniques for Estimating Cougar Predation Parameters

ABSTRACT Using clusters of locations obtained from Global Positioning System (GPS) telemetry collars to identify predation events may allow more efficient estimation of behavioral predation parameters for the study and management of large carnivore predator-prey systems. Applications of field- and model-based GPS telemetry cluster techniques, however, have met with mixed success. To further evaluate and refine these techniques for cougars (Puma concolor), we used data from visits to 1,735 GPS telemetry clusters, 637 of which were locations where cougars killed prey >8 kg in a multi-prey system in west-central Alberta. We tested 1) whether clusters were reliably created at kill locations, 2) the ability of logistic regression models to identify kill occurrence (prey >8 kg) and multinomial regression models to identify the prey species at a kill cluster, and 3) the duration of monitoring required to accurately estimate kill rate and prey composition. We found that GPS collars programmed to attempt location fixes every 3 hours consistently identified locations where prey >8 kg were handled, and cluster creation was robust to GPS location acquisition failures (poor collar fix success). The logistic regression model was capable of estimating cougar kill rate with a mean 5-fold cross validation error of <10%, provided the appropriate probability cutoff distinguishing kill clusters from non-kill clusters was selected. Logistic models also can be used to direct visits to clusters, reducing field efforts by as much as 25%, while still locating >95% of all kills. The multinomial model overpredicted occurrence of primary prey (deer) in the diet and underpredicted consumption of alternate prey (e.g., elk and moose) by as much as 100%. We conclude that a purely model-based approach should be used cautiously and that field visitation is required to obtain reliable information on species, sex, age, or condition of prey. Ultimately, we recommend a combined approach that involves using models to direct field visitation when estimating behavioral predation parameters. Regardless of the monitoring approach, long continuous monitoring periods (i.e., >100 days of a 180-day period) were necessary to reduce bias and imprecision in kill rate and prey composition estimates.     

Comparing Ground Telemetry and Global Positioning System Methods to Determine Cougar Kill Rates

ABSTRACT We assessed whether use of 2 methods, intensive very high frequency (VHF) radiotelemetry and Global Positioning System (GPS) cluster sampling, yielded similar estimates of cougar (Puma concolor) kill rates in Yellowstone National Park, 1998–2005. We additionally determined biases (underestimation or overestimation of rates) resulting from each method. We used modeling to evaluate what characteristics of clusters best predicted a kill versus no kill and further evaluated which predictor(s) minimized effort and the number of missed kills. We conducted 16 VHF ground predation sequences resulting in 37 kill intervals (KIs) and 21 GPS sequences resulting in 84 KIs on 6 solitary adult females, 4 maternal females, and 5 adult males. Kill rates (days/kill and biomass [kg] killed/day) did not differ between VHF and GPS predation sampling methods for maternal females, solitary adult females, and adult males. Sixteen of 142 (11.3%) kills detected via GPS clusters were missed through VHF ground-based sampling, and the kill rate was underestimated by an average of 5.2 (95% CI = 3.8–6.6) days/kill over all cougar social classes. Five of 142 (3.5%) kills identified by GPS cluster sampling were incorrectly identified as the focal individual's kill from scavenging, and the kill rate was overestimated within the adult male social class by an average of 5.8 (95% CI = 3.0–8.5) days/ungulate kill. The number of nights (locations between 2000 hours and 0500 hours) a cougar spent at a cluster was the most efficient variable at predicting predation, minimizing the missed kills, and minimizing number of extra clusters that needed to be searched. In Yellowstone National Park, where competing carnivores displaced cougars from their kills, it was necessary to search extra sites where a kill may not have been present to ensure we did not miss small, ungulate prey kills or kills with displacement. Using predictions from models to assign unvisited clusters as no kill, small prey kill, or large prey kill can bias downward the number of kills a cougar made and bias upward kills made by competitors that displace cougars or scavenge cougar kills. Our findings emphasize that field visitation is crucial in determining displacement and scavenging events that can result in biases when using GPS cluster methods in multicarnivore systems.     

Spatial and Temporal Clustering of Chikungunya Virus Transmission in Dominica

Using geo-referenced case data, we present spatial and spatio-temporal cluster analyses of the early spread of the 2013–2015 chikungunya virus (CHIKV) in Dominica, an island in the Caribbean. Spatial coordinates of the locations of the first 417 reported cases observed between December 15^th, 2013 and March 11^th, 2014, were captured using the Global Positioning System (GPS). We observed a preponderance of female cases, which has been reported for CHIKV outbreaks in other regions. We also noted statistically significant spatial and spatio-temporal clusters in highly populated areas and observed major clusters prior to implementation of intensive vector control programs suggesting early vector control measures, and education had an impact on the spread of the CHIKV epidemic in Dominica. A dynamical identification of clusters can lead to local assessment of risk and provide opportunities for targeted control efforts for nations experiencing CHIKV outbreaks.     

Space–time clusters for early detection of grizzly bear predation

Accurate detection and classification of predation events is important to determine predation and consumption rates by predators. However, obtaining this information for large predators is constrained by the speed at which carcasses disappear and the cost of field data collection. To accurately detect predation events, researchers have used GPS collar technology combined with targeted site visits. However, kill sites are often investigated well after the predation event due to limited data retrieval options on GPS collars (VHF or UHF downloading) and to ensure crew safety when working with large predators. This can lead to missing information from small‐prey (including young ungulates) kill sites due to scavenging and general site deterioration (e.g., vegetation growth). We used a space–time permutation scan statistic (STPSS) clustering method (SaTScan) to detect predation events of grizzly bears (Ursus arctos) fitted with satellite transmitting GPS collars. We used generalized linear mixed models to verify predation events and the size of carcasses using spatiotemporal characteristics as predictors. STPSS uses a probability model to compare expected cluster size (space and time) with the observed size. We applied this method retrospectively to data from 2006 to 2007 to compare our method to random GPS site selection. In 2013–2014, we applied our detection method to visit sites one week after their occupation. Both datasets were collected in the same study area. Our approach detected 23 of 27 predation sites verified by visiting 464 random grizzly bear locations in 2006–2007, 187 of which were within space–time clusters and 277 outside. Predation site detection increased by 2.75 times (54 predation events of 335 visited clusters) using 2013–2014 data. Our GLMMs showed that cluster size and duration predicted predation events and carcass size with high sensitivity (0.72 and 0.94, respectively). Coupling GPS satellite technology with clusters using a program based on space–time probability models allows for prompt visits to predation sites. This enables accurate identification of the carcass size and increases fieldwork efficiency in predation studies.     

AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides

Background

A tremendous amount of efforts have been devoted to identifying genes for diagnosis and prognosis of diseases using microarray gene expression data. It has been demonstrated that gene expression data have cluster structure, where the clusters consist of co-regulated genes which tend to have coordinated functions. However, most available statistical methods for gene selection do not take into consideration the cluster structure.

Results

We propose a supervised group Lasso approach that takes into account the cluster structure in gene expression data for gene selection and predictive model building. For gene expression data without biological cluster information, we first divide genes into clusters using the K-means approach and determine the optimal number of clusters using the Gap method. The supervised group Lasso consists of two steps. In the first step, we identify important genes within each cluster using the Lasso method. In the second step, we select important clusters using the group Lasso. Tuning parameters are determined using V-fold cross validation at both steps to allow for further flexibility. Prediction performance is evaluated using leave-one-out cross validation. We apply the proposed method to disease classification and survival analysis with microarray data.

Conclusion

We analyze four microarray data sets using the proposed approach: two cancer data sets with binary cancer occurrence as outcomes and two lymphoma data sets with survival outcomes. The results show that the proposed approach is capable of identifying a small number of influential gene clusters and important genes within those clusters, and has better prediction performance than existing methods.     

On Extrapolating Past the Range of Observed Data When Making Statistical Predictions in Ecology

Ecologists are increasingly using statistical models to predict animal abundance and occurrence in unsampled locations. The reliability of such predictions depends on a number of factors, including sample size, how far prediction locations are from the observed data, and similarity of predictive covariates in locations where data are gathered to locations where predictions are desired. In this paper, we propose extending Cook’s notion of an independent variable hull (IVH), developed originally for application with linear regression models, to generalized regression models as a way to help assess the potential reliability of predictions in unsampled areas. Predictions occurring inside the generalized independent variable hull (gIVH) can be regarded as interpolations, while predictions occurring outside the gIVH can be regarded as extrapolations worthy of additional investigation or skepticism. We conduct a simulation study to demonstrate the usefulness of this metric for limiting the scope of spatial inference when conducting model-based abundance estimation from survey counts. In this case, limiting inference to the gIVH substantially reduces bias, especially when survey designs are spatially imbalanced. We also demonstrate the utility of the gIVH in diagnosing problematic extrapolations when estimating the relative abundance of ribbon seals in the Bering Sea as a function of predictive covariates. We suggest that ecologists routinely use diagnostics such as the gIVH to help gauge the reliability of predictions from statistical models (such as generalized linear, generalized additive, and spatio-temporal regression models).     

Building a mechanistic understanding of predation with GPS-based movement data

Quantifying kill rates and sources of variation in kill rates remains an important challenge in linking predators to their prey. We address current approaches to using global positioning system (GPS)-based movement data for quantifying key predation components of large carnivores. We review approaches to identify kill sites from GPS movement data as a means to estimate kill rates and address advantages of using GPS-based data over past approaches. Despite considerable progress, modelling the probability that a cluster of GPS points is a kill site is no substitute for field visits, but can guide our field efforts. Once kill sites are identified, time spent at a kill site (handling time) and time between kills (killing time) can be determined. We show how statistical models can be used to investigate the influence of factors such as animal characteristics (e.g. age, sex, group size) and landscape features on either handling time or killing efficiency. If we know the prey densities along paths to a kill, we can quantify the ‘attack success’ parameter in functional response models directly. Problems remain in incorporating the behavioural complexity derived from GPS movement paths into functional response models, particularly in multi-prey systems, but we believe that exploring the details of GPS movement data has put us on the right path.     

Statistical Methods for Identifying Wolf Kill Sites Using Global Positioning System Locations

Abstract: Accurate estimates of kill rates remain a key limitation to addressing many predator—prey questions. Past approaches for identifying kill sites of large predators, such as wolves (Canis lupus), have been limited primarily to areas with abundant winter snowfall and have required intensive ground-tracking or aerial monitoring. More recently, attempts have been made to identify clusters of locations obtained using Global Positioning System (GPS) collars on predators to identify kill sites. However, because decision rules used in determining clusters have not been consistent across studies, results are not necessarily comparable. We illustrate a space—time clustering approach to statistically define clusters of wolf GPS locations that might be wolf kill sites, and we then use binary and multinomial logistic regression to model the probability of a cluster being a non—kill site, kill site of small-bodied prey species, or kill site of a large-bodied prey species. We evaluated our approach using field visits of kills and assessed the accuracy of the models using an independent dataset. The cluster-scan approach identified 42–100% of wolf-killed prey, and top logistic regression models correctly classified 100% of kills of large-bodied prey species, but 40% of small-bodied prey species were classified as nonkills. Although knowledge of prey distribution and vulnerability may help refine this approach, identifying small-bodied prey species will likely remain problematic without intensive field efforts. We recommend that our approach be utilized with the understanding that variation in prey body size and handling time by wolves will likely have implications for the success of both the cluster scan and logistic regression components of the technique. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):798–807; 2008)     

Auditory Mismatch Negativity and Repetition Suppression Deficits in Schizophrenia Explained by Irregular Computation of Prediction Error

BackgroundThe predictive coding model is rapidly gaining attention in schizophrenia research. It posits the neuronal computation of residual variance (‘prediction error’) between sensory information and top-down expectation through multiple hierarchical levels. Event-related potentials (ERP) reflect cortical processing stages that are increasingly interpreted in the light of the predictive coding hypothesis. Both mismatch negativity (MMN) and repetition suppression (RS) measures are considered a prediction error correlates based on error detection and error minimization, respectively.MethodsTwenty-five schizophrenia patients and 25 healthy controls completed auditory tasks designed to elicit MMN and RS responses that were investigated using repeated measures models and strong spatio-temporal a priori hypothesis based on previous research. Separate correlations were performed for controls and schizophrenia patients, using age and clinical variables as covariates.ResultsMMN and RS deficits were largely replicated in our sample of schizophrenia patients. Moreover, MMN and RS measures were strongly correlated in healthy controls, while no correlation was found in schizophrenia patients. Single-trial analyses indicated significantly lower signal-to-noise ratio during prediction error computation in schizophrenia.ConclusionsThis study provides evidence that auditory ERP components relevant for schizophrenia research can be reconciled in the light of the predictive coding framework. The lack of any correlation between the investigated measures in schizophrenia patients suggests a disruption of predictive coding mechanisms in general. More specifically, these results suggest that schizophrenia is associated with an irregular computation of residual variance between sensory input and top-down models, i.e. prediction error.     

Estimating cancer survival and clinical outcome based on genetic tumor progression scores

MOTIVATION: In cancer research, prediction of time to death or relapse is important for a meaningful tumor classification and selecting appropriate therapies. Survival prognosis is typically based on clinical and histological parameters. There is increasing interest in identifying genetic markers that better capture the status of a tumor in order to improve on existing predictions. The accumulation of genetic alterations during tumor progression can be used for the assessment of the genetic status of the tumor. For modeling dependences between the genetic events, evolutionary tree models have been applied. RESULTS: Mixture models of oncogenetic trees provide a probabilistic framework for the estimation of typical pathogenetic routes. From these models we derive a genetic progression score (GPS) that estimates the genetic status of a tumor. GPS is calculated for glioblastoma patients from loss of heterozygosity measurements and for prostate cancer patients from comparative genomic hybridization measurements. Cox proportional hazard models are then fitted to observed survival times of glioblastoma patients and to times until PSA relapse following radical prostatectomy of prostate cancer patients. It turns out that the genetically defined GPS is predictive even after adjustment for classical clinical markers and thus can be considered a medically relevant prognostic factor. AVAILABILITY: Mtreemix, a software package for estimating tree mixture models, is freely available for non-commercial users at http://mtreemix.bioinf.mpi-sb.mpg.de. The raw cancer datasets and R code for the analysis with Cox models are available upon request from the corresponding author.     

Predictions of native American population structure using linguistic covariates in a hidden regression framework

Background

The mainland of the Americas is home to a remarkable diversity of languages, and the relationships between genes and languages have attracted considerable attention in the past. Here we investigate to which extent geography and languages can predict the genetic structure of Native American populations.

Methodology/Principal Findings

Our approach is based on a Bayesian latent cluster regression model in which cluster membership is explained by geographic and linguistic covariates. After correcting for geographic effects, we find that the inclusion of linguistic information improves the prediction of individual membership to genetic clusters. We further compare the predictive power of Greenberg''s and The Ethnologue classifications of Amerindian languages. We report that The Ethnologue classification provides a better genetic proxy than Greenberg''s classification at the stock and at the group levels. Although high predictive values can be achieved from The Ethnologue classification, we nevertheless emphasize that Choco, Chibchan and Tupi linguistic families do not exhibit a univocal correspondence with genetic clusters.

Conclusions/Significance

The Bayesian latent class regression model described here is efficient at predicting population genetic structure using geographic and linguistic information in Native American populations.     

The effect of competing carnivores on the feeding behaviour of leopards (Panthera pardus) in an African savanna

Knowledge of competition dynamics among Africa’s large carnivores is important for conservation. However, investigating carnivore behaviour in the field can be challenging especially for species that are difficult to access. Methods that enable remote collection of data provide a means of recording natural behaviour and are therefore useful for studying elusive species such as leopards (Panthera pardus). Camera traps and Global Positioning System (GPS) collars are powerful tools often used independently to study animal behaviour but where their data are combined, the interpretation of a species’ behaviours is improved. In this study we used data from baited camera trap stations to investigate the feeding habits of leopards at Malilangwe Wildlife Reserve, Zimbabwe. We investigated the influence of spotted hyenas, lions and other competing leopards on the feeding duration of leopards using Generalized Linear Mixed Effects Modelling. To test the influence of competing predators on resting distances from bait sites, eight leopards were fitted with GPS collars. Results showed that leopards spent the shortest time feeding on the baits in the presence of competing male leopards compared to other predators while lion presence caused animals to rest farthest from bait sites. Interaction analysis indicated that small‐bodied leopards spent significantly shorter durations feeding when spotted hyenas were present. Our findings demonstrate that competition from guild carnivores has negative impacts on the food intake of leopards, which may have implications for fitness and survival. This study provides a snapshot of the competition dynamics at bait sites which may give insight to ecosystem level interactions among large carnivores in savanna ecosystems.     

Modeling female brown bear kill rates on moose calves using global positioning satellite data

The recent development in Global Positioning System (GPS) techniques has started a new era in predation studies. Estimates of kill rates based on animal movements and GPS relocation clusters have proven to be valid in several obligatory carnivores. The main focus has been to obtain accurate mean predation estimates for the management of wildlife populations. We present a model to estimate individual kill rates of moose calves by adult female brown bears in Sweden, based on spatiotemporal clustering of 30,889 bear GPS relocations and 71 moose calves verified killed during 714 field investigations in 2004–2006. In this virtually single-predator single large prey system, the omnivorous brown bear is an efficient predator on moose calves up to 4 weeks of age. The top model set only included models with cluster radii of 30 m or 50 m, indicating very high kill-site fidelity. The best model included a cluster radius of 30 m and number of periods of bear activity at the kill site as a single covariate. The mean estimated individual kill rate of 7.6 ± 0.71 (n = 18, ± SE) moose calves per calving season is comparable to the estimate of 6.8 from a previous study of radio-tracked moose in our study area, though at a lower moose/bear ratio. The mean annual kill rates varied from 6.1 to 9.4 calves per bear. The estimated individual kill rates ranged from 2 to 15 calves per season, indicating a large individual variation in hunting skills and possibly effort. Predation and livestock depredation represent a core conflict between humans and carnivores in rural Scandinavia. Accurate predation estimates represent an important step in quantifying costs of carnivores and reducing human–carnivore conflicts. Our technique may be applied in the exploration of predation mechanisms and predator–prey interactions, and contribute to the old and global debate of problem individuals in livestock depredation. © 2012 The Wildlife Society.     

A dead giveaway: Foraging vultures and other avian scavengers respond to auditory cues

Carrion represents an unpredictable and widely distributed primary food source for vultures and other avian scavengers. Avian scavengers in African savanna ecosystems are reported to rely exclusively on visual stimuli to locate carcasses. However, carnivores’ predation of large mammalian herbivores and subsequent competition for access to the carcass can result in considerable noise, often audible over long distances and for prolonged periods. Vultures and other avian scavengers may therefore detect and respond to these auditory cues, as do the mammalian carnivores alongside which vultures have coevolved, but this has not been investigated to date. Working in the Serengeti ecosystem, Tanzania, we used diurnal auditory broadcasts to simulate predation and competitive carnivore feeding interactions. Based on the current understanding of avian scavenger ecology, we hypothesized that avian responses to call‐in stations would be evoked exclusively by visual, rather than auditory, cues. We therefore predicted that (a) the arrival of avian scavengers at call‐in stations should be preceded and facilitated by mammalian carnivores and that (b) the arrival of avian scavengers would be positively correlated with the number of mammalian scavengers present, which would increase detectability. We recorded 482 birds during 122 separate playback events. In 22% of these instances, avian scavengers arrived first, ruling out responses based exclusively on visual observations of mammalian carnivores, thereby contradicting our first prediction. Furthermore, the first avian arrivals at survey sessions were inversely related to the number of hyenas and jackals present, contradicting our second prediction. Since no bait or carcasses were used during the experiments, these responses are indicative of the birds’ ability to detect and respond to audio stimuli. Our findings challenge the current consensus of sensory perception and foraging in these species and provide evidence that avian scavengers have the ability to use sound to locate food resources.     

The effect of retrospective sampling on binary regression models for clustered data

Recently a great deal of attention has been given to binary regression models for clustered or correlated observations. The data of interest are of the form of a binary dependent or response variable, together with independent variables X1,...., Xk, where sets of observations are grouped together into clusters. A number of models and methods of analysis have been suggested to study such data. Many of these are extensions in some way of the familiar logistic regression model for binary data that are not grouped (i.e., each cluster is of size 1). In general, the analyses of these clustered data models proceed by assuming that the observed clusters are a simple random sample of clusters selected from a population of clusters. In this paper, we consider the application of these procedures to the case where the clusters are selected randomly in a manner that depends on the pattern of responses in the cluster. For example, we show that ignoring the retrospective nature of the sample design, by fitting standard logistic regression models for clustered binary data, may result in misleading estimates of the effects of covariates and the precision of estimated regression coefficients.     

A mechanistic spatio-temporal modeling of COVID-19 data

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels.     

Suitability of using the global positioning system (GPS) for studying Feral Pigeons Columba livia in the urban habitat

Capsule GPS tracking gives very precise information about Feral Pigeons' spatio-temporal behaviour in the urban habitat.

Aims To test the suitability and the limits of GPS tracking in the urban habitat for a detailed analysis of Feral Pigeons' spatio-temporal behaviour.

Methods We placed ten receivers in eight different locations in the city of Basel, Switzerland. Between 1 and 23 April 2003, we performed 166 recordings and compared the stored positions with the real location. We also tested the GPS receivers on 29 free-living Feral Pigeons.

Results Almost 82% of the positions obtained with the GPS receivers were within 25 m and 96% within 100 m of the real location. The accuracy varied between locations, depending on the proportion of open sky. In 38% of the tests, no positions were stored. We performed 143 test flights with 29 Feral Pigeons (18 males and 11 females). A total of 118 flights produced ‘storable’ position information, 25 flights (17.5%) produced no storable data. Over 47% of the flights were complete (beginning and ending at loft), the others began or ended elsewhere. We encountered some difficulties: delays to get the first fix; reflection of the satellite signal on tall buildings; and limited battery life.

Conclusion Despite some difficulties related to the urban habitat and the technical features of the GPS receivers, we recommend the GPS-based tracking method for studying the spatio-temporal behaviour of Feral Pigeons and other birds weighing over 300 g and which are easy to capture.     

Wolf Movement Patterns: a Key to Estimation of Kill Rate?

Abstract: To estimate wolf (Canis lupus) kill rates from fine-scale movement patterns, we followed adult wolves in 3 territories of the Scandinavian wolf population using Global Positioning Systems (GPS) during the winters of 2001–2003. The resulting 6 datasets of 62–84 study days gave a total of 8,747 hourly GPS positions. We visited clusters of positions in the field on average 8.8 days after positioning and found moose (Alces alces) killed by wolves during the study period on 74 (8%) of the 953 clusters. The number of positions and visits to a cluster, their interaction, and the proportion of afternoon positions were significant fixed effects in mixed logistic-regression models predicting the probability of a cluster containing a wolf-killed moose. The models, however, displayed a poor goodness-of-fit and were not a suitable tool for estimating kill rates from positioning data alone. They might be used to reduce fieldwork by excluding unlikely clusters, although the reduction was not substantial. We discuss proximate factors (i.e., human disturbance and access to prey) as well as ultimate factors (i.e., social organization, intra-guild dominance, and litter size) as potential causes of the observed high temporal and spatial variation in prey-handling. For similar future kill-rate studies, we recommend increasing field efforts and shortening positioning intervals.