Biology subjects

Tucker, M. A.

Publications and source records attributed to Tucker, M. A..

4 recordsLinked to original sources

bioRxiv2025

MacroMove: a global database of animal movement across taxa and movement types

Animal movement is a key ecological process that shapes species distributions and interactions and connects resources, populations and habitats, underpinning ecosystem functioning and stability. While foraging, dispersal, and migration are the most widely studied movement types, they operate at distinct spatial and temporal scales, and no single resource currently integrates these processes across taxa, traits, and ecosystems. We present MacroMove, a comprehensive global movement database containing 92,207 observations from 4,329 (4,167 using GBIF backbone) species, spanning 36 classes and 648 families across 205 countries, with data sourced from 6,234 references and 36 meta-analyses/databases. It includes both vertebrates and invertebrates from terrestrial, freshwater, and marine environments. The database includes various movement metrics - home range area, dispersal distance, migration distance, day range, routine speed and maximum speed - including summary statistics (e.g. mean, median, maximum) where available. Records are reported at individual, population, or species levels and include standardised metadata describing species identity (binomial name, class, family), research methods (e.g. sampling method, sampling level and sampling dates) and location (e.g. country, location and coordinates). The dataset was assembled from published meta-analyses/databases and original studies included in these meta-analyses. Trait information (body mass and locomotion mode) from original studies and associated metadata was incorporated, with additional data sourced from established trait databases and reputable online resources. In the final dataset, body mass is available for 98 % and locomotion for 100% of species. Taxonomy was harmonised using the Global Biodiversity Information Facility (GBIF) backbone taxonomy. The database is supplied in comma-delimited text (.csv) format to maximise accessibility and compatibility with a wide range of workflows. By consolidating existing movement data into a single, standardised and openly accessible resource, MacroMove fills a major gap in available biodiversity data. It serves as a practical and extensible foundation for synthesising movement information across taxa and ecosystems, advancing data-driven research on the ecological and evolutionary correlates of animal movement.

ecology↗

bioRxiv2025

MoveTraits - A database for integrating animal behaviour into trait-based ecology

Trait-based approaches are key to understanding eco-evolutionary processes but rarely account for animal behaviour despite its central role in ecosystem dynamics. We propose integrating behaviour into trait-based ecology through movement traits - standardised and comparable measures of animal movement derived from biologging data, such as daily displacements or range sizes. Accounting for animal behaviour will advance trait-based research on species interactions, community structure, and ecosystem functioning. Importantly, movement traits allow for quantification of behavioural reaction norms, offering insights into species acclimation and adaptive capacity to environmental change. We outline a vision for a living global movement trait database that enhances trait data curation by (1) continuously growing alongside shared biologging data, (2) calculating traits directly from individual-level data using standardised, consistent methodology, and (3) providing information on multi-level (species, individual, within-individual) trait variation. We present a proof-of-concept MoveTraits database with 55 mammal and 108 bird species, demonstrating calculation workflows for 5 traits across multiple time scales. Movement traits have significant potential to improve trait-based global change predictions and contribute to global biodiversity assessments as Essential Biodiversity Variables. By making animal movement data more accessible and interpretable, this database could bridge the gap between movement ecology and biodiversity policy, facilitating evidence-based conservation.

ecology↗

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↗

bioRxiv2019

Does Sleep Protect Memories Against Interference? A Failure to Replicate

Across a broad spectrum of memory tasks, retention is superior following a night of sleep compared to a day of wake. However, this result alone does not clarify whether sleep merely slows the forgetting that would otherwise occur as a result of information processing during wakefulness, or whether sleep actually consolidates memories, protecting them from subsequent retroactive interference. Two influential studies (Ellenbogen, et al., 2006, 2009) suggested that sleep protects memories against the subsequent retroactive interference that occurs when participants learn new yet overlapping information (interference learning). In these studies, interference was much less detrimental to memory following a night of sleep compared to a day of wakefulness, a finding that provided strong evidence that sleep supports this important aspect of memory consolidation. In the current well-powered replication study, we repeated the protocol of Ellenbogen, et al. (2009) and, additionally, we examined the impact of intrinsic motivation on performance in sleep and wake participants. We were unable to replicate the finding that sleep protects memories against retroactive interference, with the detrimental effects of interference learning being essentially the same in wake and sleep participants. We also found that while intrinsic motivation benefitted task acquisition it was not a modulator of sleep-wake differences in memory processing. These finding of this replication study draw into question the claim that sleep protects memories against the effects of retroactive interference, and moreover, they highlight the importance of replicating key findings in the study of sleeps impact on memory processing before drawing strong conclusions that drive the direction of future research.

neuroscience↗