Biogeographic response of marine plankton to Cenozoic environmental changes.

In palaeontological studies, groups with consistent ecological and morphological traits across a clade's history (functional groups)1 afford different perspectives on biodiversity dynamics than do species and genera2,3, which are evolutionarily ephemeral. Here we analyse Triton, a global dataset of Cenozoic macroperforate planktonic foraminiferal occurrences4, to contextualize changes in latitudinal equitability gradients1, functional diversity, palaeolatitudinal specialization and community equitability. We identify: global morphological communities becoming less specialized preceding the richness increase after the Cretaceous-Palaeogene extinction; ecological specialization during the Early Eocene Climatic Optimum, suggesting inhibitive equatorial temperatures during the peak of the Cenozoic hothouse; increased specialization due to circulation changes across the Eocene-Oligocene transition, preceding the loss of morphological diversity; changes in morphological specialization and richness about 19 million years ago, coeval with pelagic shark extinctions5; delayed onset of changing functional group richness and specialization between hemispheres during the mid-Miocene plankton diversification. The detailed nature of the Triton dataset permits a unique spatiotemporal view of Cenozoic pelagic macroevolution, in which global biogeographic responses of functional communities and richness are decoupled during Cenozoic climate events. The global response of functional groups to similar abiotic selection pressures may depend on the background climatic state (greenhouse or icehouse) to which a group is adapted.

Complex nonmonotonic responses of biodiversity to habitat destruction.

It has typically been assumed that habitat destruction, characterized by habitat loss and fragmentation, has consistently negative effects on biodiversity. While numerous empirical studies have shown the detrimental effects of habitat loss, debate continues as to whether habitat fragmentation has universally negative effects. To explore the effects of habitat fragmentation, we developed a simple model for site-occupancy dynamics in fragmented landscapes. With the model, we demonstrate that a competition-colonization trade-off can result in nonlinear oscillatory responses in biodiversity to both habitat loss and fragmentation. However, the overall pattern of habitat loss reducing species richness is still established, in line with empirical observations. Interestingly, the existence of localized oscillations in biodiversity can explain the mixed responses of species richness to habitat fragmentation per se observed in nature, thereby reconciling the debate on the fragmentation-diversity relationship. Therefore, this study offers a parsimonious mechanistic explanation for empirically observed biodiversity patterns in response to habitat destruction.

Towards a mechanistic understanding of variation in aquatic food chain length.

Ecologists have long sought to understand variation in food chain length (FCL) among natural ecosystems. Various drivers of FCL, including ecosystem size, resource productivity and disturbance, have been hypothesised. However, when results are aggregated across existing empirical studies from aquatic ecosystems, we observe mixed FCL responses to these drivers. To understand this variability, we develop a unified competition-colonisation framework for complex food webs incorporating all of these drivers. With competition-colonisation tradeoffs among basal species, our model predicts that increasing ecosystem size generally results in a monotonic increase in FCL, while FCL displays non-linear, oscillatory responses to resource productivity or disturbance in large ecosystems featuring little disturbance or high productivity. Interestingly, such complex responses mirror patterns in empirical data. Therefore, this study offers a novel mechanistic explanation for observed variations in aquatic FCL driven by multiple environmental factors.

Behavioral responses of terrestrial mammals to COVID-19 lockdowns.

COVID-19 lockdowns in early 2020 reduced human mobility, providing an opportunity to disentangle its effects on animals from those of landscape modifications. Using GPS data, we compared movements and road avoidance of 2300 terrestrial mammals (43 species) during the lockdowns to the same period in 2019. Individual responses were variable with no change in average movements or road avoidance behavior, likely due to variable lockdown conditions. However, under strict lockdowns 10-day 95th percentile displacements increased by 73%, suggesting increased landscape permeability. Animals' 1-hour 95th percentile displacements declined by 12% and animals were 36% closer to roads in areas of high human footprint, indicating reduced avoidance during lockdowns. Overall, lockdowns rapidly altered some spatial behaviors, highlighting variable but substantial impacts of human mobility on wildlife worldwide.

Incorporating multidimensional behavior into a risk management tool for a critically endangered and migratory species.

Conservation of migratory species exhibiting wide-ranging and multidimensional behaviors is challenged by management efforts that only utilize horizontal movements or produce static spatial-temporal products. For the deep-diving, critically endangered eastern Pacific leatherback turtle, tools that predict where turtles have high risks of fisheries interactions are urgently needed to prevent further population decline. We incorporated horizontal-vertical movement model results with spatial-temporal kernel density estimates and threat data (gear-specific fishing) to develop monthly maps of spatial risk. Specifically, we applied multistate hidden Markov models to a biotelemetry data set (n = 28 leatherback tracks, 2004-2007). Tracks with dive information were used to characterize turtle behavior as belonging to 1 of 3 states (transiting, residential with mixed diving, and residential with deep diving). Recent fishing effort data from Global Fishing Watch were integrated with predicted behaviors and monthly space-use estimates to create maps of relative risk of turtle-fisheries interactions. Drifting (pelagic) longline fishing gear had the highest average monthly fishing effort in the study region, and risk indices showed this gear to also have the greatest potential for high-risk interactions with turtles in a residential, deep-diving behavioral state. Monthly relative risk surfaces for all gears and behaviors were added to South Pacific TurtleWatch (SPTW) (https://www.upwell.org/sptw), a dynamic management tool for this leatherback population. These modifications will refine SPTW's capability to provide important predictions of potential high-risk bycatch areas for turtles undertaking specific behaviors. Our results demonstrate how multidimensional movement data, spatial-temporal density estimates, and threat data can be used to create a unique conservation tool. These methods serve as a framework for incorporating behavior into similar tools for other aquatic, aerial, and terrestrial taxa with multidimensional movement behaviors.

Incorporación del comportamiento multidimensional a una herramienta de gestión de riesgos para una especie migratoria en peligro crítico Resumen La conservación de especies migratorias con comportamientos amplios y multidimensionales se enfrenta a los esfuerzos de gestión que sólo utilizan movimientos horizontales o que producen resultados espaciotemporales estáticos. La tortuga laúd, una especie de las profundidades en peligro crítico, necesita con urgencia herramientas que pronostiquen los lugares en donde las tortugas tienen mayor riesgo de interactuar con las pesquerías para prevenir una mayor declinación poblacional. Incorporamos los resultados de un modelo de movimiento horizontal-vertical a las estimaciones de la densidad del núcleo espaciotemporal y de los datos de amenaza (equipo de pesca específico) para desarrollar mapas mensuales del riesgo espacial. De manera más concreta, aplicamos modelos ocultos multiestado de Markov a un conjunto de datos de biotelemetría (n=28 rastros de tortugas laúd, 2004-2007). Usamos los rastros con información de inmersión para caracterizar el comportamiento de las tortugas como uno de tres estados: en tránsito, inmersión mixta o por residencia e inmersión profunda o por residencia. Integramos los datos recientes del esfuerzo de pesca tomados de Global Fishing Watch a los comportamientos pronosticados y las estimaciones del uso mensual del espacio para crear mapas del riesgo relativo de las interacciones tortuga-pesquería. La pesca con palangre de deriva (pelágica) tuvo el promedio mensual más alto de esfuerzo de pesca en la región de estudio. Los índices de riesgo indicaron que este equipo también tiene el potencial más elevado de interacciones de alto riesgo con las tortugas en estado residencial o de inmersión profunda. Añadimos los comportamientos y las superficies de riesgo relativo mensuales a South Pacific Turtle Watch (SPTW) (https://www.upwell.org/sptw), una herramienta dinámica para la gestión de esta población de laúdes. Estos cambios pulirán la capacidad de SPTW para proporcionar predicciones importantes de las áreas con potencial alto de riesgo de pesca accesoria para las tortugas con comportamientos específicos. Nuestros resultados demuestran cómo los datos de movimiento multidimensional, las estimaciones de densidad espaciotemporal y los datos de amenaza pueden ser usados para crear una herramienta única de conservación. Estos métodos sirven como marco para incorporar el comportamiento a herramientas similares para otros taxones acuáticos, aéreos y terrestres con comportamientos multidimensionales.

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19.

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supply-constrained resource allocation strategies for controlling novel disease epidemics. To address the challenge of constrained resource optimization for managing diseases with complications like pre- and asymptomatic transmission, we develop an integro partial differential equation compartmental disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals. Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission. To analyze the influence of these realistic features on disease controllability, we find optimal strategies for reducing total infection sizes that allocate limited testing resources between 'clinical' testing, which targets symptomatic individuals, and 'non-clinical' testing, which targets non-symptomatic individuals. We apply our model not only to the original, delta, and omicron COVID-19 variants, but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions, which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness. We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies, while the relationship between incubation-latent mismatch, controllability, and optimal strategies is complicated. In particular, though greater degrees of presymptomatic transmission reduce disease controllability, they may increase or decrease the role of non-clinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length. Importantly, our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.

Satellite Tracking of Head-Started Juvenile Green Turtles (Chelonia mydas) Reveals Release Effects and an Ontogenetic Shift.

Juveniles of marine species, such as sea turtles, are often understudied in movement ecology. To determine dispersal patterns and release effects, we released 40 satellite-tagged juvenile head-started green turtles (Chelonia mydas, 1-4 years) from two separate locations (January and July 2023) off the coast of the Cayman Islands. A statistical model and vector plots were used to determine drivers of turtle directional swimming persistence and the role of ocean current direction. More than half (N = 22) effectively dispersed in 6-22 days from the islands to surrounding areas. The January turtles radiated out (185-1138 km) in distinct directions in contrast to the northward dispersal of the July turtles (27-396 km). Statistical results and vector plots supported that daily swimming persistence increased towards the end of tracks and near coastal regions, with turtles largely swimming in opposition to ocean currents. These results demonstrate that captive-reared juvenile greens have the ability to successfully navigate towards key coastal developmental habitats. Differences in dispersal (January vs. July) further support the importance of release timing and location. Our results inform conservation of the recovering Caymanian green turtles and we advise on how our methods can be improved and modified for future sea turtle and juvenile movement ecology studies.

Reinforced diffusions as models of memory-mediated animal movement.

How memory shapes animals' movement paths is a topic of growing interest in ecology, with connections to planning for conservation and climate change. Empirical studies suggest that memory has both temporal and spatial components, and can include both attractive and aversive elements. Here, we introduce reinforced diffusions (the continuous time counterpart of reinforced random walks) as a modelling framework for understanding the role that memory plays in determining animal movements. This framework includes reinforcement via functions of time before present and of distance away from a current location. Focusing on the interplay between memory and central place attraction (a component of home ranging behaviour), we explore patterns of space usage that result from the reinforced diffusion. Our efforts identify three qualitatively different behaviours: bounded wandering behaviour that does not collapse spatially, collapse to a very small area, and, most intriguingly, convergence to a cycle. Subsequent applications show how reinforced diffusion can create movement trajectories emulating the learning of movement routes by homing pigeons and consolidation of ant travel paths. The mathematically explicit manner with which assumptions about the structure of memory can be stated and subsequently explored provides linkages to biological concepts like an animal's 'immediate surroundings' and memory decay.

Late Cenozoic cooling restructured global marine plankton communities.

The geographic ranges of marine organisms, including planktonic foraminifera1, diatoms, dinoflagellates2, copepods3 and fish4, are shifting polewards owing to anthropogenic climate change5. However, the extent to which species will move and whether these poleward range shifts represent precursor signals that lead to extinction is unclear6. Understanding the development of marine biodiversity patterns over geological time and the factors that influence them are key to contextualizing these current trends. The fossil record of the macroperforate planktonic foraminifera provides a rich and phylogenetically resolved dataset that provides unique opportunities for understanding marine biogeography dynamics and how species distributions have responded to ancient climate changes. Here we apply a bipartite network approach to quantify group diversity, latitudinal specialization and latitudinal equitability for planktonic foraminifera over the past eight million years using Triton, a recently developed high-resolution global dataset of planktonic foraminiferal occurrences7. The results depict a global, clade-wide shift towards the Equator in ecological and morphological community equitability over the past eight million years in response to temperature changes during the late Cenozoic bipolar ice sheet formation. Collectively, the Triton data indicate the presence of a latitudinal equitability gradient among planktonic foraminiferal functional groups which is coupled to the latitudinal biodiversity gradient only through the geologically recent past (the past two million years). Before this time, latitudinal equitability gradients indicate that higher latitudes promoted community equitability across ecological and morphological groups. Observed range shifts among marine planktonic microorganisms1,2,8 in the recent and geological past suggest substantial poleward expansion of marine communities even under the most conservative future global warming scenarios.

Sampling bias and the robustness of ecological metrics for plant-damage-type association networks.

Plants and their insect herbivores have been a dominant component of the terrestrial ecological landscape for the past 410 million years and feature intricate evolutionary patterns and co-dependencies. A complex systems perspective allows for both detailed resolution of these evolutionary relationships as well as comparison and synthesis across systems. Using proxy data of insect herbivore damage (denoted by the damage type or DT) preserved on fossil leaves, functional bipartite network representations provide insights into how plant-insect associations depend on geological time, paleogeographical space, and environmental variables such as temperature and precipitation. However, the metrics measured from such networks are prone to sampling bias. Such sensitivity is of special concern for plant-DT association networks in paleontological settings where sampling effort is often severely limited. Here, we explore the sensitivity of functional bipartite network metrics to sampling intensity and identify sampling thresholds above which metrics appear robust to sampling effort. Across a broad range of sampling efforts, we find network metrics to be less affected by sampling bias and/or sample size than richness metrics, which are routinely used in studies of fossil plant-DT interactions. These results provide reassurance that cross-comparisons of plant-DT networks offer insights into network structure and function and support their widespread use in paleoecology. Moreover, these findings suggest novel opportunities for using plant-DT networks in neontological terrestrial ecology to understand functional aspects of insect herbivory across geological time, environmental perturbations, and geographic space.

Implicit versus explicit vector management strategies in models for vector-borne disease epidemiology.

Throughout the vector-borne disease modeling literature, there exist two general frameworks for incorporating vector management strategies (e.g. area-wide adulticide spraying and larval source reduction campaigns) into vector population models, namely, the "implicit" and "explicit" control frameworks. The more simplistic "implicit" framework facilitates derivation of mathematically rigorous results on disease suppression and optimal control, but the biological connection of these results to real-world "explicit" control actions that could guide specific management actions is vague at best. Here, we formally define a biological and mathematical relationship between implicit and explicit control, and we provide mathematical expressions relating the strength of implicit control to management-relevant properties of explicit control for four common intervention strategies. These expressions allow the optimal control and basic reproduction number analyses typically utilized in implicit control modeling to be interpreted directly in terms of real-world actions and real-world monetary costs. Our methods reveal that only certain sub-classes of explicit control protocols are able to be represented as implicit controls, and that implicit control is a meaningful approximation of explicit control only when resonance-like synergistic effects between multiple explicit controls have negligible effects on population reduction. When non-negligible synergy exists, implicit control results, despite their mathematical tidiness, fail to provide accurate predictions regarding vector control and disease spread. Collectively, these elements build an effective bridge between analytically interesting and mathematically tractable implicit control and the challenging, action-oriented explicit control.

Animal soundscapes reveal key markers of Amazon forest degradation from fire and logging.

Safeguarding tropical forest biodiversity requires solutions for monitoring ecosystem structure over time. In the Amazon, logging and fire reduce forest carbon stocks and alter habitat, but the long-term consequences for wildlife remain unclear, especially for lesser-known taxa. Here, we combined multiday acoustic surveys, airborne lidar, and satellite time series covering logged and burned forests (n = 39) in the southern Brazilian Amazon to identify acoustic markers of forest degradation. Our findings contradict expectations from the Acoustic Niche Hypothesis that animal communities in more degraded habitats occupy fewer "acoustic niches" defined by time and frequency. Instead, we found that aboveground biomass was not a consistent proxy for acoustic biodiversity due to the divergent patterns of "acoustic space occupancy" between logged and burned forests. Ecosystem soundscapes highlighted a stark, and sustained reorganization in acoustic community assembly after multiple fires; animal communication networks were quieter, more homogenous, and less acoustically integrated in forests burned multiple times than in logged or once-burned forests. These findings demonstrate strong biodiversity cobenefits from protecting burned Amazon forests from recurrent fire. By contrast, soundscape changes after logging were subtle and more consistent with acoustic community recovery than reassembly. In both logged and burned forests, insects were the dominant acoustic markers of degradation, particularly during midday and nighttime hours, which are not typically sampled by traditional biodiversity field surveys. The acoustic fingerprints of degradation history were conserved across replicate recording locations, indicating that soundscapes may offer a robust, taxonomically inclusive solution for digitally tracking changes in acoustic community composition over time.

Analyses of three-dimensional species associations reveal departures from neutrality in a tropical forest.

The study of community spatial structure is central to understanding diversity patterns over space and species co-occurrence at local scales. Although most analytical approaches consider horizontal and vertical dimensions separately, in this study we introduce a three-dimensional spatial analysis that simultaneously includes horizontal and vertical species associations. Using tree census data (2000-2016) and allometries from the Luquillo forest plot in Puerto Rico, we show that spatial organization becomes less random over time as the forest recovered from land-use legacy effects and hurricane disturbance. Tree species vertical segregation is predominant in the forest with almost all species that co-occur in the horizontal plane avoiding each other in the vertical dimension. Horizontal segregation is less common than vertical, whereas three-dimensional aggregation (a proxy for direct tree competition) is the least frequent type of spatial association. Furthermore, dominant species are involved in more non-random spatial associations, implying that species co-occurrence is facilitated by species segregation in space. This novel three-dimensional analysis allowed us to identify and quantify tree species spatial distributions, how interspecific competition was reduced through forest structure, and how it changed over time after disturbance, in ways not detectable from two-dimensional analyses alone.

A better index for analysis of co-occurrence and similarity.

Scientists often need to know whether pairs of entities tend to occur together or independently. Standard approaches to this issue use co-occurrence indices such as Jaccard, Sørensen-Dice, and Simpson. We show that these indices are sensitive to the prevalences of the entities they describe and that this invalidates their interpretability. We propose an index, α, that is insensitive to prevalences. Published datasets reanalyzed with both α and Jaccard's index (J) yield profoundly different biological inferences. For example, a published analysis using J contradicted predictions of the island biogeography theory finding that community stability increased with increasing physical isolation. Reanalysis of the same dataset with the estimator [Formula: see text] reversed that result and supported theoretical predictions. We found similarly marked effects in reanalyses of antibiotic cross-resistance and human disease biomarkers. Our index α is not merely an improvement; its use changes data interpretation in fundamental ways.

Higher-order effects, continuous species interactions, and trait evolution shape microbial spatial dynamics.

The assembly and maintenance of microbial diversity in natural communities, despite the abundance of toxin-based antagonistic interactions, presents major challenges for biological understanding. A common framework for investigating such antagonistic interactions involves cyclic dominance games with pairwise interactions. The incorporation of higher-order interactions in such models permits increased levels of microbial diversity, especially in communities in which antibiotic-producing, sensitive, and resistant strains coexist. However, most such models involve a small number of discrete species, assume a notion of pure cyclic dominance, and focus on low mutation rate regimes, none of which well represent the highly interlinked, quickly evolving, and continuous nature of microbial phenotypic space. Here, we present an alternative vision of spatial dynamics for microbial communities based on antagonistic interactions-one in which a large number of species interact in continuous phenotypic space, are capable of rapid mutation, and engage in both direct and higher-order interactions mediated by production of and resistance to antibiotics. Focusing on toxin production, vulnerability, and inhibition among species, we observe highly divergent patterns of diversity and spatial community dynamics. We find that species interaction constraints (rather than mobility) best predict spatiotemporal disturbance regimes, whereas community formation time, mobility, and mutation size best explain patterns of diversity. We also report an intriguing relationship among community formation time, spatial disturbance regimes, and diversity dynamics. This relationship, which suggests that both higher-order interactions and rapid evolution are critical for the origin and maintenance of microbial diversity, has broad-ranging links to the maintenance of diversity in other systems.

Inclement weather forces stopovers and prevents migratory progress for obligate soaring migrants.

BACKGROUND: Migrating birds experience weather conditions that change with time, which affect their decision to stop or resume migration. Soaring migrants are especially sensitive to changing weather conditions because they rely on the availability of environmental updrafts to subsidize flight. The timescale that local weather conditions change over is on the order of hours, while stopovers are studied at the daily scale, creating a temporal mismatch. METHODS: We used GPS satellite tracking data from four migratory Turkey Vulture (Cathartes aura) populations, paired with local weather data, to determine if the decision to stopover by migrating Turkey Vultures was in response to changing local weather conditions. We analyzed 174 migrations of 34 individuals from 2006 to 2019 and identified 589 stopovers based on variance of first passage times. We also investigated if the extent of movement activity correlated with average weather conditions experienced during a stopover, and report general patterns of stopover use by Turkey Vultures between seasons and across populations. RESULTS: Stopover duration ranged from 2 h to more than 11 days, with 51 % of stopovers lasting < 24 h. Turkey Vultures began stopovers immediately in response to changes in weather variables that did not favor thermal soaring (e.g., increasing precipitation fraction and decreasing thermal updraft velocity) and their departure from stopovers was associated with improvements in weather that favored thermal development. During stopovers, proportion of activity was negatively associated with precipitation but was positively associated with temperature and thermal updraft velocity. CONCLUSIONS: The rapid response of migrating Turkey Vultures to changing weather conditions indicates weather-avoidance is one of the major functions of their stopover use. During stopovers, however, the positive relationship between proportion of movement activity and conditions that promote thermal development suggests not all stopovers are used for weather-avoidance. Our results show that birds are capable of responding rapidly to their environment; therefore, for studies interested in external drivers of weather-related stopovers, it is essential that stopovers be identified at fine temporal scales.

Exploring the functional composition of the human microbiome using a hand-curated microbial trait database.

BACKGROUND: Even when microbial communities vary wildly in their taxonomic composition, their functional composition is often surprisingly stable. This suggests that a functional perspective could provide much deeper insight into the principles governing microbiome assembly. Much work to date analyzing the functional composition of microbial communities, however, relies heavily on inference from genomic features. Unfortunately, output from these methods can be hard to interpret and often suffers from relatively high error rates. RESULTS: We built and analyzed a domain-specific microbial trait database from known microbe-trait pairs recorded in the literature to better understand the functional composition of the human microbiome. Using a combination of phylogentically conscious machine learning tools and a network science approach, we were able to link particular traits to areas of the human body, discover traits that determine the range of body areas a microbe can inhabit, and uncover drivers of metabolic breadth. CONCLUSIONS: Domain-specific trait databases are an effective compromise between noisy methods to infer complex traits from genomic data and exhaustive, expensive attempts at database curation from the literature that do not focus on any one subset of taxa. They provide an accurate account of microbial traits and, by limiting the number of taxa considered, are feasible to build within a reasonable time-frame. We present a database specific for the human microbiome, in the hopes that this will prove useful for research into the functional composition of human-associated microbial communities.

Deciphering trophic interactions in a mid-Cambrian assemblage.

Exceptionally preserved fossil sites have allowed specimen-based identification of trophic interactions to which network analyses have been applied. However, network analyses of the fossil record suffer from incomplete and indirect data, time averaging that obscures species coexistence, and biases in preservation. Here, we present a high-resolution fossil data set from Raymond Quarry member of the mid-Cambrian Burgess Shale (7,549 specimens, 61 taxa, ∼510 Mya) and formulate a measure of "preservation bias" that aids identification of assemblage subsets to which network analyses can be reliably applied. For these sections, abundance correlation network analyses predicted longitudinally consistent trophic and competitive interactions. Our analyses predicted previously postulated trophic interactions with 83.5% accuracy and demonstrated a shift from specialist interaction-dominated assemblages to ones dominated by generalist and competitive interactions. This approach provides a robust, taphonomically corrected framework to explore and predict in detail the existence and ecological character of putative interactions in fossil data sets.

Resource selection of a nomadic ungulate in a dynamic landscape.

Nomadic movements are often a consequence of unpredictable resource dynamics. However, how nomadic ungulates select dynamic resources is still understudied. Here we examined resource selection of nomadic Mongolian gazelles (Procapra gutturosa) in the Eastern Steppe of Mongolia. We used daily GPS locations of 33 gazelles tracked up to 3.5 years. We examined selection for forage during the growing season using the Normalized Difference Vegetation Index (NDVI). In winter we examined selection for snow cover which mediates access to forage and drinking water. We studied selection at the population level using resource selection functions (RSFs) as well as on the individual level using step-selection functions (SSFs) at varying spatio-temporal scales from 1 to 10 days. Results from the population and the individual level analyses differed. At the population level we found selection for higher than average NDVI during the growing season. This may indicate selection for areas with more forage cover within the arid steppe landscape. In winter, gazelles selected for intermediate snow cover, which may indicate preference for areas which offer some snow for hydration but not so much as to hinder movement. At the individual level, in both seasons and across scales, we were not able to detect selection in the majority of individuals, but selection was similar to that seen in the RSFs for those individuals showing selection. Difficulty in finding selection with SSFs may indicate that Mongolian gazelles are using a random search strategy to find forage in a landscape with large, homogeneous areas of vegetation. The combination of random searches and landscape characteristics could therefore obscure results at the fine scale of SSFs. The significant results on the broader scale used for the population level RSF highlight that, although individuals show uncoordinated movement trajectories, they ultimately select for similar vegetation and snow cover.