Biology subjects

Toledo, S.

Publications and source records attributed to Toledo, S..

6 recordsLinked to original sources

bioRxiv2024

An Information Theory Framework for Movement Path Segmentation and Analysis

Improved animal tracking technologies provide opportunities for novel segmentation of movement tracks/paths into behavioral activity modes (BAMs) critical to understanding the ecology of individuals and the functioning of ecosystems. Current BAM segmentation includes biological change point analyses and hidden Markov models. Here we use an elemental approach to segmenting tracks into {micro}-step-long "base segments" and m-base-segment-long "words." These are respectively clustered into n statistical movement elements (StaMEs) and k "raw" canonical activity modes (CAMs). Once the words are coded using m extracted StaME symbols, those encoded by the same string of symbols, after a rectification processes has been implemented to minimize misassigned words, are identified with particular "rectified" CAM types. The percent of reassignment errors, along with information theory measures, are used to compare the efficiencies of coding both simulated and empirical barn owl data for a selection of parameter values and approaches to clustering.

ecology↗

bioRxiv2024

Combining radio-telemetry and radar measurements to test optimal foraging in an aerial insectivore bird

Optimal foraging theory posits that foragers adjust their movements based on prey abundance to optimize food intake. While extensively studied in terrestrial and marine environments, aerial foraging has remained relatively unexplored due to technological limitations. This study, uniquely combining BirdScan-MR1 radar and the ATLAS biotelemetry system, investigates the foraging dynamics of Little Swifts (Apus affinis) in response to insect movements over Israels Hula Valley. Insect Movement Traffic Rate (MoTR) substantially varied across days, strongly influencing swift movement. On days with high MoTR, swifts exhibited reduced flight distance, increased colony visit rate, and earlier arrivals at the breeding colony, reflecting a dynamic response to prey availability. However, no significant effects were observed in total foraging duration, flight speed, or daily route length. Notably, as insect abundance increased, inter-individual distances decreased. These findings suggest that Little Swifts optimize their foraging behavior in relation to aerial insect abundance, likely influencing reproductive success and population dynamics. The integration of radar technology and biotelemetry systems provides a unique perspective on the interactions between aerial insectivores and their prey, contributing to a comprehensive understanding of optimal foraging strategies in diverse environments.

ecology↗

bioRxiv2023

The Statistical Building Blocks of Animal Movement Simulations

Animal movement plays a key role in many ecological processes and has a direct influence on an individuals fitness at several scales of analysis (i.e., next-step, subdiel, day-by-day, seasonal). This high-lights the need to dissect movement behavior at different spatio-temporal scales and develop hierarchical movement tools for generating realistic tracks to supplement existing single-temporal-scale simulators. In reality, animal movement paths are a concatenation of fundamental movement elements (FuMEs: e.g., a step or wing flap), but these are not generally extractable from a relocation time-series track (e.g., sequential GPS fixes) from which step-length (SL, aka velocity) and turning-angle (TA) time series can be extracted. For short, fixed-length segments of track, we generate their SL and TA statistics (e.g., means, standard deviations, correlations) to obtain segment-specific vectors that can be cluster into different types. We use the centroids of these clusters to obtain a set of statistical movement elements (StaMEs; e.g. directed fast movement versus random slow movement elements) that we use as a basis for analyzing and simulating movement tracks. Our novel concept is that sequences of StaMEs provide a basis for constructing and fitting step-selection kernels at the scale of fixed-length canonical activity modes: short fixed-length sequences of interpretable activity such as dithering, ambling, directed walking, or running. Beyond this, variable length pure or characteristic mixtures of CAMs can be interpreted as behavioral activity modes (BAMs), such as gathering resources (a sequence of dithering and walking StaMEs) or beelining (a sequence of fast directed-walk StaMEs interspersed with vigilance and navigation stops). Here we formulate a multi-modal, step-selection kernel simulation framework, and construct a 2-mode movement simulator (Numerus ANIMOVER_1), using Numerus RAMP technology. We also illustrate methods for extracting StaMEs from both simulated and real data from two barn owls (Tyto alba) in the Harod Valley, Israel. Overall, our new bottom-up approach to path segmentation allows us to both dissect real movement tracks and generate realistic synthetic ones, thereby providing a general tool for testing hypothesis in movement ecology and simulating animal movement in diverse contexts such as evaluating an individuals response to landscape changes, release of an individual into a novel environment, or identifying when individuals are sick or unusually stressed.

ecology↗

bioRxiv2021

WATLAS: high resolution and real-time tracking of many small birds in the Dutch Wadden Sea

BackgroundTracking animal movement is important for understanding how animals interact with their (changing) environment, and crucial for predicting and explaining how animals are affected by anthropogenic effects. The Wadden Sea is a UNESCO World Heritage Site and a region of global importance for millions of small shorebirds. Due to climate change and anthropogenic activity, understanding and predicting movement and space-use in areas like the Wadden Sea is increasingly important. Monitoring and predicting animal movement, however, requires high-resolution tracking of many individuals. While high-resolution tracking has been made possible through GPS, trade-offs between tag weight and battery life limit its use to larger species. MethodsHere, we introduce WATLAS (the Wadden Sea deployment of the ATLAS tracking system) capable of monitoring the movements of hundreds of (small) birds simultaneously in the Dutch Wadden Sea. WATLAS employs an array of receiver stations that can detect and localise small, low-cost tags at fine spatial (meters) and temporal resolution (seconds). From 2017-2021, we tracked red knots, sanderlings, bar-tailed godwits, and common terns. We use parts of these data to give four examples on its performance and how WATLAS can be used to study numerous aspects of animal behaviour, such as, space-use (both intra- and inter-specific), among-individual variation, and social networks. ResultsAfter describing the WATLAS system, we first illustrate space-use of red knots across the study area and how the tidal environment affects their movement. Secondly, we show large among-individual differences in distances travelled per day, and thirdly illustrate how high-throughput WATLAS data allows calculating a proximity-based social network. Finally, we demonstrate that using WATLAS to monitor multiple species can reveal differential space use. For example, despite sanderlings and red knots roosting together, they foraged in different areas of the mudflats. ConclusionsThe high-resolution tracking data collected by WATLAS offers many possibilities for research into the drivers of bird movement in the Wadden Sea. WATLAS could provide a tool for impact assessment, and thus aid nature conservation and management of the globally important Wadden Sea ecosystem.

ecology↗

bioRxiv2021

Validating a high-throughput tracking system: ATLAS as a regional-scale alternative to GPS

O_LIFine-scale tracking of animal movement is important to understand the proximate mechanisms of animal behaviour. While GPS tracking is an excellent tool for measuring animal movement, trade-offs between tag weight, cost and lifespan limit its application to relatively large species, a small number of individuals or short tracking durations, respectively. The reverse-GPS system - ATLAS - uses lighter, cheaper tags compared to GPS tags, that can also last long periods of time at high sampling frequencies. Six systems are now operational worldwide and have successfully tracked over 50 species in various landscape types. This growing use of ATLAS to track animal movement motivates further refinement of best-practice application and an assessment of its accuracy. C_LIO_LIHere, we test the accuracy and precision of the largest ATLAS system, located in the Dutch Wadden Sea using concurrent GPS measurements as a reference. This large-scale ATLAS system consists of 26 receivers and covers 1326 km2 of intertidal region, with almost no physical obstacles for radio signals, providing a useful baseline for other systems. To measure accuracy, we calculated the distance between ATLAS and GPS location estimates for a route (mobile test) and 16 fixed locations (stationary test) on the Griend mudflat. C_LIO_LIATLAS-derived location estimates differed on average 4.2 m from GPS-estimated stationary test sites and 5.7 m from GPS tracks taken whilst moving between them. Signals that were collected by more receiver stations were more accurate, although even 3-receiver localisations were comparable with GPS localisations ([~]10 m difference). Higher receiver stations detected the tag at longer distances. C_LIO_LIFuture ATLAS users should consider the height of receivers, their spatial arrangement, density and the movement mode of the study species (e.g., ground-dwelling or flying). In conclusion, ATLAS provides an accurate, regional-scale alternative to global GPS-based tracking with which hundreds of relatively small-bodied species can be tracked simultaneously for long periods of time. Our study shows that ATLAS is a valid alternative, providing comparable location estimates to GPS. C_LI

ecology↗

bioRxiv2020

A Guide to Pre-Processing High-Throughput Animal Tracking Data

O_LIModern, high-throughput animal tracking studies collect increasingly large volumes of data at very fine temporal scales. At these scales, location error can exceed the animals step size, leading to mis-estimation of key movement metrics such as speed. Cleaning the data to reduce location errors prior to analyses is one of the main ways movement ecologists deal with noisy data, and has the advantage of being more scalable to massive datasets than more complex methods. Though data cleaning is widely recommended, and ecologists routinely consider cleaned data to be the ground-truth, inclusive uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is still rather scarce. C_LIO_LIA pipeline for cleaning massive high-throughput datasets must balance ease of use and computationally efficient signal vs. noise screening, in which location errors are rejected without discarding valid animal movements. Another useful feature of a pre-processing pipeline is efficiently segmenting and clustering location data for statistical methods, while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time consuming, and to boost reproducibility, a robust pre-processing pipeline must be automated. C_LIO_LIIn this article we provide guidance on building pipelines for pre-processing high-throughput animal tracking data in order to prepare it for subsequent analysis. Our recommended pipeline, consisting of removing outliers, smoothing the filtered result, and thinning it to a uniform sampling interval, is applicable to many massive tracking datasets. We apply this pipeline to simulated movement data with location errors, and also show a case study of how large volumes of cleaned data can be transformed into biologically meaningful residence patches, for quick biological inference on animal space use. We use calibration data to illustrate how pre-processing improves its quality, and to verify that the residence patch synthesis accurately captures animal space use. Finally, turning to tracking data from Egyptian fruit bats (Rousettus aegyptiacus), we demonstrate the pre-processing pipeline and residence patch method in a fully worked out example. C_LIO_LITo help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre-processing pipeline and atlastools can be used with any high-throughput animal movement data in which the high data-volume combined with knowledge of the tracked individuals movement capacity can be used to reduce location errors. The atlastools function is easy to use for beginners, while providing a template for further development. The use of common pre-processing steps that are simple yet robust promotes standardised methods in the field of movement ecology and leads to better inferences from data. C_LI

ecology↗