Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses.
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ABSTRACT:
SUBMITTER: Signer J
PROVIDER: S-EPMC6362447 | biostudies-literature | 2019 Jan
REPOSITORIES: biostudies-literature
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Project description:Understanding movement patterns and home range of species is paramount in ecology; it is particularly important for threatened taxa as it can provide valuable information for conservation management. To address this knowledge gap for a range-restricted endangered bird, we estimated home range size, daily movement patterns and habitat use of a granivorous subspecies in northeast Australia, the black-throated finch (Poephila cincta cincta; BTF) using radio-tracking and re-sighting of colour banded birds. Little is known about basic aspects of its ecology including movement patterns and home range sizes. From 2011-2014 we colour-banded 102 BTF and radio-tracked 15 birds. We generated home ranges (calculated using kernel and Minimum Convex Polygons techniques of the 15 tracked BTF). More than 50% of the re-sightings occurred within 200 m of the banding site (n = 51 out of 93 events) and within 100 days of capture. Mean home-range estimates with kernel (50%, 95% probability) and Minimum Convex Polygons were 10.59 ha, 50.79 ha and 46.27 ha, respectively. Home range size differed between two capture sites but no seasonal differences were observed. BTF home ranges overlapped four habitat types among eight available. Habitat selection was different from random at Site 1 (χ2 = 373.41, df = 42, p<0.001) and Site 2 (χ2 = 1896.1, df = 45, p<0.001); however, the preferred habitats differed between the two sites. BTF moved further than expected on the basis of current knowledge, with three individuals being resighted over 15 km from the banding location. However, BTF maintain small home ranges over short time-frames. Occasional long-distance movements may be related to resource bottleneck periods. Daily movement patterns differed between sites, which is likely linked to the fact that the sites differ in the spatial distribution of resources. The work provides information about home range sizes and local movement of BTF that will be valuable for targeting effective management and conservation strategies for this endangered granivore.
| S-EPMC5130248 | biostudies-literature
Project description:BackgroundNew wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods.MethodsWe used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing.ResultsWe demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs.ConclusionsBased on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.
| S-EPMC8063450 | biostudies-literature
Project description:Acoustic telemetry is a principle tool for observing aquatic animals, but coverage over large spatial scales remains a challenge. To resolve this, Australia has implemented the Integrated Marine Observing System's Animal Tracking Facility which comprises a continental-scale hydrophone array and coordinated data repository. This national acoustic network connects localized projects, enabling simultaneous monitoring of multiple species over scales ranging from 100?s of meters to 1000?s of kilometers. There is a need to evaluate the utility of this national network in monitoring animal movement ecology, and to identify the spatial scales that the network effectively operates over. Cluster analyses assessed movements and residency of 2181 individuals from 92 species, and identified four functional movement classes apparent only through aggregating data across the entire national network. These functional movement classes described movement metrics of individuals rather than species, and highlighted the plasticity of movement patterns across and within populations and species. Network analyses assessed the utility and redundancy of each component of the national network, revealing multiple spatial scales of connectivity influenced by the geographic positioning of acoustic receivers. We demonstrate the significance of this nationally coordinated network of receivers to better reveal intra-specific differences in movement profiles and discuss implications for effective management.
| S-EPMC5829234 | biostudies-literature
Project description:BackgroundSelecting animals for breeding in the optimum way plays an essential role for the management of genetic resources and in selective breeding of livestock species. It requires to compute the optimum genetic contribution of each selection candidate to the next generation. Current software packages for optimum contribution selection (OCS) are not able to handle the main conflicting objectives of animal breeding programs simultaneously, which includes to increase genetic gain, to increase or to maintain genetic diversity, to recover the original genetic background of endangered breeds with historic introgression, and to maintain or increase genetic diversity at native alleles.ResultsThe free R package optiSel offers functions for estimating the above mentioned parameters from pedigree and marker data, and for solving OCS problems. One parameter can be optimized, whereas the remaining ones can be constrained. The results reveal the optimum numbers of offspring of all selection candidates, and can subsequently be used for mate allocation. Different solvers can be used. Solver slsqp was superior when the genetic diversity at native alleles was to be maximized, whereas solvers cccp and cccp2 were superior for all other OCS problems.ConclusionOptimum contribution selection applied to local breeds requires special attention due to the conflicting objectives of their breeding programs. The free R package optiSel is an easy-to-use software taking these conflicting objectives into account.
| S-EPMC6332575 | biostudies-literature
Project description:Patterns of dispersal behavior are often driven by the composition and configuration of suitable habitat in a matrix of unsuitable habitat. Interactions between animal behavior and landscapes can therefore influence population dynamics, population and species distributions, population genetic structure, and the evolution of behavior. Spatially explicit individual-based models (IBMs) are ideal tools for exploring the effects of landscape structure on dispersal. We developed an empirically parameterized IBM in the modeling framework SEARCH to simulate dispersal of translocated American martens in Wisconsin. We tested the hypothesis that a time-limited disperser should be willing to settle in lower quality habitat over time. To evaluate model performance, we used a pattern-oriented modeling approach. Our best model matched all empirical dispersal patterns (e.g., dispersal distance) except time to settlement. This model incorporated a required search phase as well as the mechanism for declining habitat selectivity over time, which represents the first demonstration of this hypothesis for a vertebrate species. We suggest that temporal plasticity in habitat selectivity allows individuals to maximize fitness by making a tradeoff between habitat quality and risk of mortality. Our IBM is pragmatic in that it addresses a management need for a species of conservation concern. However, our model is also paradigmatic in that we explicitly tested a theory of dispersal behavior. Linking these 2 approaches to ecological modeling can further the utility of individual-based modeling and provide direction for future theoretical and empirical work on animal behavior.
| S-EPMC6450207 | biostudies-literature
Project description:Given the global decline of many invertebrate food resources, it is fundamental to understand the dietary requirements of insectivores. We give new insights into the functional relationship between the spatial habitat use, food availability, and diet of a crepuscular aerial insectivore, the European Nightjar (Caprimulgus europaeus) by relating spatial use data with high-throughput sequencing (HTS) combined with DNA metabarcoding. Our study supports the predictions that nightjars collect a substantial part of their daily nourishment from foraging locations, sometimes at considerable distance from nesting sites. Lepidopterans comprise 65% of nightjars' food source. Nightjars tend to select larger species of Lepidoptera (>19 mm) which suggests that nightjars optimize the efficiency of foraging trips by selecting the most energetically favorable-larger-prey items. We anticipate that our findings may shed additional light on the interactions between invertebrate communities and higher trophic levels, which is required to understand the repercussions of changing food resources on individual- and population-level processes.
| S-EPMC7713983 | biostudies-literature
Project description:BackgroundThe nervous functions of an organism are primarily reflected in the behavior it is capable of. Measuring behavior quantitatively, at high-resolution and in an automated fashion provides valuable information about the underlying neural circuit computation. Accordingly, computer-vision applications for animal tracking are becoming a key complementary toolkit to genetic, molecular and electrophysiological characterization in systems neuroscience.Methodology/principal findingsWe present Sensory Orientation Software (SOS) to measure behavior and infer sensory experience correlates. SOS is a simple and versatile system to track body posture and motion of single animals in two-dimensional environments. In the presence of a sensory landscape, tracking the trajectory of the animal's sensors and its postural evolution provides a quantitative framework to study sensorimotor integration. To illustrate the utility of SOS, we examine the orientation behavior of fruit fly larvae in response to odor, temperature and light gradients. We show that SOS is suitable to carry out high-resolution behavioral tracking for a wide range of organisms including flatworms, fishes and mice.Conclusions/significanceOur work contributes to the growing repertoire of behavioral analysis tools for collecting rich and fine-grained data to draw and test hypothesis about the functioning of the nervous system. By providing open-access to our code and documenting the software design, we aim to encourage the adaptation of SOS by a wide community of non-specialists to their particular model organism and questions of interest.
| S-EPMC3415430 | biostudies-literature
Project description:The increasing spatiotemporal accuracy of Global Navigation Satellite Systems (GNSS) tracking systems opens the possibility to infer animal behaviour from tracking data. We studied the relationship between high-frequency GNSS data and behaviour, aimed at developing an easily interpretable classification method to infer behaviour from location data. Behavioural observations were carried out during tracking of cows (Bos Taurus) fitted with high-frequency GPS (Global Positioning System) receivers. Data were obtained in an open field and forested area, and movement metrics were calculated for 1 min, 12 s and 2 s intervals. We observed four behaviour types (Foraging, Lying, Standing and Walking). We subsequently used Classification and Regression Trees to classify the simultaneously obtained GPS data as these behaviour types, based on distances and turning angles between fixes. GPS data with a 1 min interval from the open field was classified correctly for more than 70% of the samples. Data from the 12 s and 2 s interval could not be classified successfully, emphasizing that the interval should be long enough for the behaviour to be defined by its characteristic movement metrics. Data obtained in the forested area were classified with a lower accuracy (57%) than the data from the open field, due to a larger positional error of GPS locations and differences in behavioural performance influenced by the habitat type. This demonstrates the importance of understanding the relationship between behaviour and movement metrics, derived from GNSS fixes at different frequencies and in different habitats, in order to successfully infer behaviour. When spatially accurate location data can be obtained, behaviour can be inferred from high-frequency GNSS fixes by calculating simple movement metrics and using easily interpretable decision trees. This allows for the combined study of animal behaviour and habitat use based on location data, and might make it possible to detect deviations in behaviour at the individual level.
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Project description:Conditional logistic regression (CLR) is widely used to analyze habitat selection and movement of animals when resource availability changes over space and time. Observations used for these analyses are typically autocorrelated, which biases model-based variance estimation of CLR parameters. This bias can be corrected using generalized estimating equations (GEE), an approach that requires partitioning the data into independent clusters. Here we establish the link between clustering rules in GEE and their effectiveness to remove statistical biases in variance estimation of CLR parameters. The current lack of guidelines is such that broad variation in clustering rules can be found among studies (e.g., 14-450 clusters) with unknown consequences on the robustness of statistical inference. We simulated datasets reflecting conditions typical of field studies. Longitudinal data were generated based on several parameters of habitat selection with varying strength of autocorrelation and some individuals having more observations than others. We then evaluated how changing the number of clusters impacted the effectiveness of variance estimators. Simulations revealed that 30 clusters were sufficient to get unbiased and relatively precise estimates of variance of parameter estimates. The use of destructive sampling to increase the number of independent clusters was successful at removing statistical bias, but only when observations were temporally autocorrelated and the strength of inter-individual heterogeneity was weak. GEE also provided robust estimates of variance for different magnitudes of unbalanced datasets. Our simulations demonstrate that GEE should be estimated by assigning each individual to a cluster when at least 30 animals are followed, or by using destructive sampling for studies with fewer individuals having intermediate level of behavioural plasticity in selection and temporally autocorrelated observations. The simulations provide valuable information to build reliable habitat selection and movement models that allow for robustness of statistical inference without removing excessive amounts of ecological information.
| S-EPMC5233429 | biostudies-literature
Project description:Agricultural activity, urban development and habitat alteration have caused the disappearance of the western spadefoot (Spea hammondii) from 80% of its geographic range in southern California. Despite the western spadefoot's continuing decline, little research has been conducted on its natural history. The home range of adult spadefoots is unknown, and their use of upland habitat is poorly understood. Both factors are important for the long-term conservation of the species because adult spadefoots spend the majority of their lives away from breeding pools in self-excavated burrows. Between January 2012 and January 2013, we surgically implanted radio transmitters in 15 spadefoots at two locations and recorded their movements and habitat use. The mean distance moved between burrow locations was 18 m (SD ± 24.1 m, range1-204 m). The mean distance of burrows from the breeding pools was 40 m (SD ± 37.42 m, range 1-262 m). Rain was a significant predictor of spadefoot movement, with more rain predicting higher probability of movement and larger distances moved. At remote sensing scale (1 m) spadefoots selected grassland habitat for their burrow locations. At the microsite scale (< 1 m) spadefoots strongly selected duff over grass or shrub cover. Spadefoots burrowed in friable, sandy/loam soil with significantly less clay than random pseudoabsence points. This research enhances our understanding of a little-studied species and will contribute to the development of effective management plans for the western spadefoot.
| S-EPMC6776459 | biostudies-literature