Biology subjects

Spiegel, O.

Publications and source records attributed to Spiegel, O..

8 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

Factors affecting the individual probability of infection with a prevalent pathogen (Mycoplasma) and the effect on Griffon vultures' movement behavior

Mycoplasmas are known as commensals and pathogenic bacteria of various raptor species causing clinical or subclinical infections. However, little is known about the prevalence of mycoplasma in captive and wild raptors and its significance to their health. In Israel, the Griffon vulture (Gyps fulvus; hereafter Griffons) is considered critically endangered, and its intensive management program includes population monitoring and restocking (captive-born or imported rehabilitated wild Spanish Griffons). Here we survey the prevalence of Mycoplasma species in both the wild and captive populations. During 2019-2020, we collected 244 tracheal swab samples from 167 unique individuals. We used PCR analysis to identify Mycoplasma species. First, we identified nine spp., including species not yet described in Israel or for Griffon vultures. Second, imported Griffons showed a higher prevalence and a different diversity of species in comparison to the local ones, suggesting that at least one Mycoplasma species (Sp 18b) was introduced into the native population. Third, juvenile Griffons had a higher prevalence, different species composition, and stronger reduction in movement compared to adults, confirming the susceptibility of this group to mycoplasma infections. GPS-tracking of 60 free-ranging individuals showed that even in the absence of apparent clinical signs, Griffons infected with mycoplasma, and especially sub-adults flew less (shorter distances and periods). These findings underscore the importance of considering potential pathogen introductions in population reinforcement and reintroduction initiatives, providing valuable insights for similar conservation programs globally. Further, they demonstrate the potential of long-term tracking for detecting subclinical effects that are unnoticeable in clinical examination.

ecology↗

bioRxiv2024

Extreme temperatures impede the release success of captive-bred avian scavengers

Conservation translocations (reinforcements and reintroductions) are central for managing various endangered species, yet, their implementation is logistically and financially challenging. Because many translocations fail due to the mortality of released individuals, identifying and preventing these factors is crucial. Here we examine risk factors affecting the post-release survival of the Griffon vulture (Gyps fulvus). The Israeli population is facing extinction, and the recovery efforts by the local Nature and Parks Authority include supplemental releases of individuals from captive-breeding or rehabilitated birds (mostly imported from Spain). We use GPS tracking and thermometers to compare movement, behavior, and weather conditions experienced by released and wild-born individuals active in the same area and time. Our results show that the Judean Desert release site had a significantly lower survival rate than other release sites included in the program. After excluding several possible factors (e.g. known pathogens) with pathological examinations, we found that released individuals at this area were exposed to higher temperatures preceding their death (compared to wild-born griffons nearby), suggesting heat stress as the cause of death. Arguably, they failed to avoid the harsh environmental conditions, resulting in overheating due to their inexperience and undeveloped flight ability, as reflected by their lower probabilities of flying and shorter travel distances per day compared to wild-born vultures. These findings have led to adjustments of the local release protocol, (namely winter releases) resulting in a significant improvement in the early survival of translocated Griffons in the Judean Desert. Accounting for the harmful effects of extreme weather conditions is particularly important in a world facing climate change. More broadly, this sequence of scientific investigation with data integration from ecological, clinical, and biotelemetry sources, leading to improvement in translocation success demonstrates how conservation practices can be optimized by supporting studies to ensure the survival of endangered species in the wild.

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↗

bioRxiv2023

Association between movement patterns, microbiome diversity, and potential pathogen presence in free-ranging feral pigeons foraging in dairy farms.

The feedback between host behavior and disease transmission is well acknowledged, but empirical studies demonstrating associations between individuals pathogens or microbiota composition and their movement are rare. We investigated these associations in feral pigeons (Columba livia domestica), a synanthrope species known to host a plethora of zoonotic pathogens. We captured pigeons in three dairy farms along an urbanization gradient in central Israel. We combined GPS-tracking with Next Generation Sequencing to characterize pigeons movement and microbiota, respectively. We found that pigeons roosted primarily in human settlements, with frequent visits to dairy farms and other agricultural sites. Microbiota diversity and composition varied between sites and the individuals within them, and several pathogens relevant to poultry, cattle, and human-health were frequently detected. Pigeons in the urban site covered shorter distances and carried a greater diversity of bacteria compared to those in rural sites. Intriguingly, beyond these among-site differences, exploratory individuals (measured by the number of unique locations they visited) had more diverse microbiota. We conclude that pigeons can potentially serve as transmission vectors among wildlife, livestock, and humans . Further, the associations between host behavior and microbiota diversity emphasize the relevance of wildlife movement analyses for disease ecology and One Health.

zoology↗

bioRxiv2022

Clarifying space use concepts in ecology: range vs. occurrence distributions

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animals home range, and many methods of estimating home ranges have arisen over the past half century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as Kernel Density Estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian Bridge Movement Models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of applying these two classes of space use estimators appropriately and distinctly. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home-range estimates quantified using occurrence estimators are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use range estimators to estimate the area of home ranges and occurrence estimators to answer other questions in movement ecology, such as when and where an animal crosses a linear feature, visits a location of interest, or interacts with other animals. Open Research StatementTracking data on Aepyceros melampus, Beatragus hunteri, Bycanistes bucinator, Cerdocyon thous, Eulemur rufifrons, Glyptemys insculpta, Gyps coprotheres, Madoqua guentheri, Ovis canadensis, Propithecus verreauxi, Sus scrofa, and Ursus arctos are publicly archived in the Dryad repository (Noonan et al. 2018; https://doi.org/10.5061/dryad.v5051j2), as are data from Procapra gutturosa (Fleming et al. 2014a; https://doi.org/10.5061/dryad.45157). Data on Panthera onca were taken from (Morato et al. 2018). Additional data are publicly archived in the Movebank repository under the following identifiers: Canis latrans, 8159699; Canis lupus, 8159399; Chrysocyon brachyurus, 18156143; Felis silvestris, 40386102; Gyps africanus, 2919708; Lepus europaeus, 25727477; Martes pennanti, 2964494; Panthera leo, 220229; Papio cynocephalus, 222027; Syncerus caffer, 1764627; Tapirus terrestris, 443607536; Torgos tracheliotus, 2919708; and Ursus americanus, 8170674.

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↗

bioRxiv2020

Estimating encounter location distributions from animal tracking data

O_LIEcologists have long been interested in linking individual behavior with higher-level processes. For motile species, this upscaling is governed by how well any given movement strategy maximizes encounters with positive factors, and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modeling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounter rates while no theoretical framework exists for directly relating individual movement with the spatial locations of encounter events in the environment. C_LIO_LIHere, we bridge this gap by introducing a new theoretical concept describing the long-term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open source software, and demonstrate the broad ecological relevance of this novel concept. C_LIO_LIWe first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation-based scenarios that occur routinely in biological systems: i) a population of individuals with home ranges that overlap with neighbors; ii) a pair of individuals with a hard territorial border between their home ranges; and iii) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white-faced capuchins (Cebus capucinus) tracked on Barro Colorado Island, Panama, and sleepy lizards (Tiliqua rugosa) tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the location-specific potential for competition, and/or identify any changes in behaviour that directly result from location-specific encounter probability. C_LIO_LIThis novel target distribution enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialised data collection protocols. This method is now openly available via the ctmm R package. C_LI

ecology↗