Emerging Network-Based Tools in Movement Ecology

Spatial connectivity of reef manta rays across the Raja Ampat archipelago, Indonesia

The reef manta ray Mobula alfredi is present throughout most island groups that form the Raja Ampat archipelago, Indonesia. The species is protected regionally and nationally and is currently managed as a single homogeneous population within the 6.7 million ha archipelago. However, scientific evidence is currently lacking regarding the spatial connectivity and population structure of M. alfredi within this archipelago. Using network analysis and an array of 34 acoustic receivers deployed throughout Raja Ampat between February 2016 and September 2021, we examined the movements of 72 subadult and adult M. alfredi tagged in seven regions of Raja Ampat. A total of 1094 M. alfredi movements were recorded and were primarily concentrated between nearby receiver stations, highlighting frequent local movements within, and limited long-distance movements between regional acoustic receiver arrays. Network analysis revealed highly connected nodes acting as hubs important for M. alfredi movements. A community detection algorithm further indicated clusters within the network. Our results suggest the existence of a metapopulation comprising three demographically and geographically distinct subpopulations within the archipelago. They also reveal the importance of Eagle Rock as a critical node in the M. alfredi movement network, justifying the urgent inclusion of this site within the Raja Ampat marine protected area network.

Modelling animal network data in R using STRAND

There have been recent calls for wider application of generative modelling approaches in applied social network analysis. At present, however, it remains difficult for typical end users-for example, field researchers-to implement generative network models, as there is a dearth of openly available software packages that make application of such models as simple as other, permutation-based approaches. Here, we outline the STRAND R package, which provides a suite of generative models for Bayesian analysis of animal social network data that can be implemented using simple, base R syntax. To facilitate ease of use, we provide a tutorial demonstrating how STRAND can be used to model proportion, count or binary network data using stochastic block models, social relation models or a combination of the two modelling frameworks. STRAND facilitates the application of generative network models to a broad range of data found in the animal social networks literature.

Landscape use by large grazers in a grassland is restructured by wildfire

Animals navigate landscapes based on perceived risks vs. rewards, as inferred from features of the landscape. In the wild, knowing how strongly animal movement is directed by landscape features is difficult to ascertain but widespread disturbances such as wildfires can serve as natural experiments. We tested the hypothesis that wildfires homogenize the risk/reward landscape, causing movement to become less directed, given that fires reduce landscape complexity as habitat structures (e.g., tree cover, dense brush) are burned. We used satellite imagery of a research reserve in Northern California to count and categorize paths made primarily by mule deer (Odocoileus hemionus) in grasslands. Specifically, we compared pre-wildfire (August 2014) and post-wildfire (September 2018) image history layers among locations that were or were not impacted by wildfire (i.e., a Before/After Control/Impact design). Wildfire significantly altered spatial patterns of deer movement: more new paths were gained and more old paths were lost in areas of the reserve that were impacted by wildfire; movement patterns became less directed in response to fire, suggesting that the risk/reward landscape became more homogenous, as hypothesized. We found evidence to suggest that wildfire affects deer populations at spatial scales beyond their scale of direct impact and raises the interesting possibility that deer perceive risks and rewards at different spatial scales. In conclusion, our study provides an example of how animals integrate spatial information from the environment to make movement decisions, setting the stage for future work on the broader ecological implications for populations, communities, and ecosystems, an emerging interest in ecology.

Migration timing and marine space use of an anadromous Arctic fish (Arctic Char, Salvelinus alpinus) revealed by local spatial statistics and network analysis

BACKGROUND

The ice-free season (typically late-June to early-October) is crucial for anadromous species of fish in the Arctic, including Arctic Char (Salvelinus alpinus), which must acquire adequate resources for growth, reproduction, and survival during a brief period of feeding in the marine environment. Arctic Char is an important food fish for Inuit communities across the Arctic. Understanding drivers and patterns of migration in the marine environment is thus essential for conservation and management of the species.

METHODS

We used passive acoustic telemetry to characterize migration patterns of 51 individual anadromous Arctic Char during the ice-free season in the marine environment of Coronation Gulf (Nunavut, Canada; 2019-2022). Based on recent genetic evidence, some tagged individuals were likely Dolly Varden (Salvelinus malma malma), a closely related species to Arctic Char. Using local Getis G* and network analysis, we described movement patterns and identified high-use locations in the marine environment. We also related freshwater overwintering location to migration timing and movement pattern.

RESULTS

Comparing groups of fish that overwintered in distinct locations, we found: (i) limited evidence that marine movements were associated with overwintering location; (ii) minor differences in use of marine space; and, (iii) timing of freshwater return differed significantly between overwintering groups, and was related to length and difficulty of the migratory pathway in freshwater. Results from both network analysis and local Getis G* revealed that, regardless of overwintering location, coastal locations were highly used by fish.

CONCLUSIONS

Overwintering locations, and the migratory routes to access overwintering locations, affect the timing of freshwater return. Preference of fish for coastal marine locations is likely due to abundance of forage and patterns in break-up of sea ice. Similarities in marine space use and movement patterns present challenges for managing this and other mixed stock fisheries of anadromous Salvelinus spp. Absences or periods of time when fish were not detected prevented comprehensive assessment of movement patterns. Local Getis G*, a local indicator of spatial association, is a helpful tool in identifying locations associated with absences in acoustic telemetry arrays, and is a complementary method to network analysis.

Finding navigation cues near fishways

Many fish species depend on migration for various parts of their life cycle. Well-known examples include diadromous fish such as salmon and eels that need both fresh water and salt water to complete their life cycle. Migration also occurs within species that depend only on fresh water. In recent decades, anthropogenic pressures on freshwater systems have increased greatly, and have resulted, among other effects, in drastic habitat fragmentation. Fishways have been developed to mitigate the resulting habitat fragmentation, but these are not always effective. To improve fishway efficiency, the variety of navigation cues used by fish must be better understood: fish use a multitude of sensory inputs ranging from flow variables to olfactory cues. The reaction of a fish is highly dependent on the intensity of the cue, the fish species involved, and individual traits. Recently developed monitoring technologies allow us to gain insights into different combinations of environmental and physiological conditions. By combining fish behavioural models with environmental models, interactions among these components can be investigated. Several methods can be used to analyse fish migration, with state-space models, hidden Markov models, and individual-based models potentially being the most relevant since they can use individual data and can tie them to explicit spatial locations within the considered system. The aim of this review is to analyse the navigational cues used by fish and the models that can be applied to gather knowledge on these processes. Such knowledge could greatly improve the design and operation of fishways for a wider range of fish species and conditions.

What acoustic telemetry can and cannot tell us about fish biology

Acoustic telemetry (AT) has become ubiquitous in aquatic monitoring and fish biology, conservation, and management. Since the early use of active ultrasonic tracking that required researchers to follow at a distance their species of interest, the field has diversified considerably, with exciting advances in both hydrophone and transmitter technology. Once a highly specialized methodology, however, AT is fast becoming a generalist tool for those wishing to study or conserve fishes, leading to diversifying application by non-specialists. With this transition in mind, we evaluate exactly what AT has become useful for, discussing how the technological and analytical advances around AT can address important questions within fish biology. In doing so, we highlight the key ecological and applied research areas where AT continues to reveal crucial new insights and, in particular, when combined with complimentary research approaches. We provide a comprehensive breakdown of the state of the art for applications of AT, discussing the ongoing challenges, where its strengths lie, and how future developments may revolutionize fisheries management, behavioral ecology and species protection. Through selected papers we illustrate specific applications across the broad spectrum of fish biology. By bringing together the recent and future developments in this field under categories designed to broadly capture many aspects of fish biology, we hope to offer a useful guide for the non-specialist practitioner as they attempt to navigate the dizzying array of considerations and ongoing developments within this diverse toolkit.

Rhythm of relationships in a social fish over the course of a full year in the wild

BACKGROUND

Animals are expected to adjust their social behaviour to cope with challenges in their environment. Therefore, for fish populations in temperate regions with seasonal and daily environmental oscillations, characteristic rhythms of social relationships should be pronounced. To date, most research concerning fish social networks and biorhythms has occurred in artificial laboratory environments or over confined temporal scales of days to weeks. Little is known about the social networks of wild, freely roaming fish, including how seasonal and diurnal rhythms modulate social networks over the course of a full year. The advent of high-resolution acoustic telemetry enables us to quantify detailed social interactions in the wild over time-scales sufficient to examine seasonal rhythms at whole-ecosystems scales. Our objective was to explore the rhythms of social interactions in a social fish population at various time-scales over one full year in the wild by examining high-resolution snapshots of a dynamic social network.

METHODS

To that end, we tracked the behaviour of 36 adult common carp, Cyprinus carpio, in a 25 ha lake and constructed temporal social networks among individuals across various time-scales, where social interactions were defined by proximity. We compared the network structure to a temporally shuffled null model to examine the importance of social attraction, and checked for persistent characteristic groups over time.

RESULTS

The clustering within the carp social network tended to be more pronounced during daytime than nighttime throughout the year. Social attraction, particularly during daytime, was a key driver for interactions. Shoaling behavior substantially increased during daytime in the wintertime, whereas in summer carp interacted less frequently, but the interaction duration increased. Therefore, smaller, characteristic groups were more common in the summer months and during nighttime, where the social memory of carp lasted up to two weeks.

CONCLUSIONS

We conclude that social relationships of carp change diurnally and seasonally. These patterns were likely driven by predator avoidance, seasonal shifts in lake temperature, visibility, forage availability and the presence of anoxic zones. The techniques we employed can be applied generally to high-resolution biotelemetry data to reveal social structures across other fish species at ecologically realistic scales.

Bighorn sheep associations: understanding tradeoffs of sociality and implications for disease transmission

Sociality directly influences mating success, survival rates, and disease, but ultimately likely evolved for its fitness benefits in a challenging environment. The tradeoffs between the costs and benefits of sociality can operate at multiple scales, resulting in different interpretations of animal behavior. We investigated the influence of intrinsic (e.g., relatedness, age) and extrinsic factors (e.g., land cover type, season) on direct contact (simultaneous GPS locations ≤ 25 m) rates of bighorn sheep (Ovis canadensis) at multiple scales near the Waterton-Glacier International Peace Park. During 2002-2012, male and female bighorn were equipped with GPS collars. Indirect contact (GPS locations ≤ 25 m regardless of time) networks identified two major breaks whereas direct contact networks identified an additional barrier in the population, all of which corresponded with prior disease exposure metrics. More direct contacts occurred between same-sex dyads than female-male dyads and between bighorn groups with overlapping summer home ranges. Direct contacts occurred most often during the winter-spring season when bighorn traveled at low speeds and when an adequate number of bighorn were collared in the area. Direct contact probabilities for all dyad types were inversely related to habitat quality, and differences in contact probability were driven by variables related to survival such as terrain ruggedness, distance to escape terrain, and canopy cover. We provide evidence that probabilities of association are higher when there is greater predation risk and that contact analysis provides valuable information for understanding fitness tradeoffs of sociality and disease transmission potential.

An integrated model for prediction of hydrologic anomalies for habitat suitability of overwintering geese in a large floodplain wetland, China

Climate anomalies and increasing human activities cause a high frequency of extreme hydrological events in wetlands, which has put waterbirds under greater survival pressure than ever. Therefore, it is crucial to predict the impact of this phenomenon on the habitat suitability of waterbirds. This study investigated the response of the goose distribution probability to hydrological variations using the flood duration index (FD), enhanced vegetation index (EVI), and waterbirds GPS tracking data in Poyang Lake. An overwintering geese habitat suitability index (HSI) is built based on the FD, EVI, and threat index and verifies the accuracy of the model simulation. Then, the effects of drought and flood on the goose habitat especially sub-lakes with different connectivity were analyzed. The findings reveal that in dry and flood years, geese will broaden their range of feeding vegetation (more fresh or mature vegetation) in response to environmental deterioration. Both drought and flood can lead to a decline in the HSI, especially flood. Connected sub-lakes are more vulnerable to hydrological anomalies than controlled sub-lakes. This research establishes a scientific foundation for floodplain wetland hydrology management and waterbird conservation.

Residency and space use estimation methods based on passive acoustic telemetry data

Acoustic telemetry has helped overcome many of the challenges faced when studying the movement ecology of aquatic species, allowing to obtain unprecedented amounts of data. This has made it into one of the most widely used methods nowadays. Many ways to analyse acoustic telemetry data have been made available and deciding on how to analyse the data requires considering the type of research objectives, relevant properties of the data (e.g., resolution, study design, equipment), habits of the study species, researcher experience, among others. To ease this decision process, here we showcase (1) some of the methods used to estimate pseudo-positions and positions from raw acoustic telemetry data, (2) methods to estimate residency and (3) methods to estimate two-dimensional home and occurrence range using geometric or hull-based methods and density-distribution methods, a network-based approach, and three-dimensional methods. We provide examples of some of these were tested using a sample of real data. With this we intend to provide the necessary background for the selection of the method(s) that better fit specific research objectives when using acoustic telemetry.

The ties that bind the sagebrush biome: integrating genetic connectivity into range-wide conservation of greater sage-grouse

Conserving genetic connectivity is fundamental to species persistence, yet rarely is made actionable into spatial planning for imperilled species. Climate change and habitat degradation have added urgency to embrace connectivity into networks of protected areas. Our two-step process integrates a network model with a functional connectivity model, to identify population centres important to maintaining genetic connectivity then to delineate those pathways most likely to facilitate connectivity thereamong for the greater sage-grouse (Centrocercus urophasianus), a species of conservation concern ranging across eleven western US states and into two Canadian provinces. This replicable process yielded spatial action maps, able to be prioritized by importance to maintaining range-wide genetic connectivity. We used these maps to investigate the efficacy of 3.2 million ha designated as priority areas for conservation (PACs) to encompass functional connectivity. We discovered that PACs encompassed 41.1% of cumulative functional connectivity-twice the amount of connectivity as random-and disproportionately encompassed the highest-connectivity landscapes. Comparing spatial action maps to impedances to connectivity such as cultivation and woodland expansion allows both planning for future management and tracking outcomes from past efforts.

Assessing neophobia and exploration while accounting for social context: an example application in scimitar-horned oryx

Spatial neophobia and exploration are often assessed in nonhuman animals by measuring behavioral responses to novel environments. These traits may especially affect the performance of individuals translocated to novel environments for conservation purposes. Here, we present methods to administer and analyze a minimally invasive novel environment test that accounts for the social context of focal individuals. We used an aerial platform to capture video footage of a captive herd of scimitar-horned oryx (Oryx dammah) entering an unfamiliar enclosure. We analyzed footage for seven individually identifiable oryx, scoring their behavioral responses (i.e., latency to enter the enclosure, and movement and posture after entering the enclosure) and social context (i.e., relative position and number of nearby animals). We performed a principal components analysis (PCA) to explore individual traits and responses, and used generalized linear mixed models (GLMMs) to determine the effect of individual traits and social context on individual posture and movement behaviors. Both PCA and GLMMs supported our expectation that social context affects individual behavior: high neighbor density and relative position were negatively related to individual movement, and variation in social context was positively related with head-up postures and movement. Oryx were well differentiated along two principal components that reflected (1) vigilance or caution, and (2) changing social context and age. These methods provide a framework for assessing individual responses to a novel environment in a group setting, which can inform reintroduction and wildlife management efforts, while minimizing interference with animal behavior and management operations.

Applying novel connectivity networks to wood turtle populations to provide comprehensive conservation management strategies for species at risk

Genetic diversity within and among populations is frequently used in prioritization processes to rank populations based on their vulnerability or distinctiveness, however, connectivity and gene flow are rarely considered within these frameworks. Using a wood turtle (Glyptemys insculpta) population graph, we introduce BRIDES as a new tool to evaluate populations for conservation purpose without focusing solely on individual nodes. BRIDES characterizes different types of shortest paths among the nodes of a subgraph and compares the shortest paths among the same nodes in a complete network. The main objectives of this study were to (1) introduce a BRIDES selection process to assist conservation biologists in the prioritization of populations, and (2) use different centrality indices and node removal statistics to compare BRIDES results and assess gene flow among wood turtle populations. We constructed six population subgraphs and used a stepwise selection algorithm to choose the optimal number of additional nodes, representing different populations, required to maximize network connectivity under different weighting schemes. Our results demonstrate the robustness of the BRIDES selection process for a given scenario, while inconsistencies were observed among node-based metrics. Results showed repeated selection of certain wood turtle populations, which could have not been predicted following only genetic diversity and distinctiveness estimation, node-based metrics and node removal analysis. Contrary to centrality measures focusing on static networks, BRIDES allowed for the analysis of evolving networks. To our knowledge, this study is the first to apply graph theory for turtle conservation genetics. We show that population graphs can reveal complex gene flow dynamics and population resiliency to local extinction. As such, BRIDES offers an interesting complement to node-based metrics and node removal to better understand the global processes at play when addressing population prioritization frameworks.

Migratory network reveals unique spatial-temporal migration dynamics of Dunlin subspecies along the East Asian-Australasian Flyway

Determining the dynamics of where and when individuals occur is necessary to understand population declines and identify critical areas for populations of conservation concern. However, there are few examples where a spatially and temporally explicit model has been used to evaluate the migratory dynamics of a bird population across its entire annual cycle. We used geolocator-derived migration tracks of 84 Dunlin (Calidris alpina) on the East Asian-Australasian Flyway (EAAF) to construct a migratory network describing annual subspecies-specific migration patterns in space and time. We found that Dunlin subspecies exhibited unique patterns of spatial and temporal flyway use. Spatially, C. a. arcticola predominated in regions along the eastern edge of the flyway (e.g., western Alaska and central Japan), whereas C. a. sakhalina predominated in regions along the western edge of the flyway (e.g., N China and inland China). No individual Dunlin that wintered in Japan also wintered in the Yellow Sea, China seas, or inland China, and vice-versa. However, similar proportions of the 4 subspecies used many of the same regions at the center of the flyway (e.g., N Sakhalin Island and the Yellow Sea). Temporally, Dunlin subspecies staggered their south migrations and exhibited little temporal overlap among subspecies within shared migration regions. In contrast, Dunlin subspecies migrated simultaneously during north migration. South migration was also characterized by individuals stopping more often and for more days than during north migration. Taken together, these spatial-temporal migration dynamics indicate Dunlin subspecies may be differentially affected by regional habitat change and population declines according to where and when they occur. We suggest that the migration dynamics presented here are useful for guiding on-the-ground survey efforts to quantify subspecies’ use of specific sites, and to estimate subspecies’ population sizes and long-term trends. Such studies would significantly advance our understanding of Dunlin space-time dynamics and the coordination of Dunlin conservation actions across the EAAF.

A model for leveraging animal movement to understand spatio-temporal disease dynamics

The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model 'Movement-driven modelling of spatio-temporal infection risk' (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.

Suitable Habitat Dynamics of Wintering Geese in a Large Floodplain Wetland: Insights from Flood Duration

The relationship between hydrological variation and the habitat use of waterbirds in wetland complexes is a significant field of ecological research. Quantification of the relationships between wetland hydrological attributes and waterbirds distribution is critical for the success of waterbird conservation. In this study, flood duration (FD) derived from synthetic aperture radar (SAR) imagery was combined with geese GPS tracking data to quantify the optimal FD thresholds for identifying geese habitats. Based on the thresholds, we defined the suitable habitats of wintering geese and investigated the difference in the spatial distribution pattern of habitat from 2018 to 2020 in Poyang Lake, China. We also considered the role of sub-lakes in habitat protection. The results showed that the area of suitable habitats for wintering geese decreased in both dry and wet years, and the range of optimal FD threshold was wider in normal years than in both dry and wet years. The proportion of suitable habitats per unit area was greater in the sub-lakes than in the whole Poyang Lake. We concluded that FD indices extracted from SAR data are valuable for reflecting the influence of the pattern of hydrological variation on waterbird distribution and for the protection and rational use of wetland ecosystems.

Temporal Graphs and Temporal Network Characteristics for Bio-Inspired Networks during Optimization

Temporal network analysis and time evolution of network characteristics are powerful tools in describing the changing topology of dynamic networks. This paper uses such approaches to better visualize and provide analytical measures for the changes in performance that we observed in Voronoi-type spatial coverage, particularly for the example of time-evolving networks with a changing number of wireless sensors being deployed. Specifically, our analysis focuses on the role different combinations of impenetrable obstacles and environmental noise play in connectivity and overall network structure. It is shown how the use of (i) temporal network graphs, and (ii) network centrality and regularity measures illustrate the differences between various options developed for the balancing act of energy and time efficiency in network coverage. Last, we compare the outcome of these measures with the less abstract classification variables, such as percent area covered and cumulative distance traveled.

Movements and residency of Caribbean reef sharks at a remote atoll in Belize, Central America

We investigated spatial use patterns of 77 Caribbean reef sharks (Carcharhinus perezi) at Lighthouse Reef Atoll, Belize over 7 years using residency patterns, kernel density (KD) estimation and network analysis. We found a high degree individual variation in spatial use of the atoll, but there were significant differences in residency and activity space between sexes, with females being overall more resident. Ontogenetic shifts in movement and residency were largely limited to females, as the residency index increased and activity space estimates decreased as females matured, while for males there was no relationship between space use or residency and size. KD analysis revealed many mature females were highly resident to discrete locations, and average activity space of the intermediate-sized sharks was significantly larger than that of the adults, but not the smallest sharks. Markov chain analyses indicated that the southwestern portion of the atoll was the most important movement corridor for all sharks. Both the Blue Hole and Half Moon Caye Natural Monuments provide some protection for larger Caribbean reef sharks; however, a gear ban on longlines on the southwestern forereef between Long Caye and the channel entrance to the Blue Hole would maximize the benefits for all sharks.

Cognitive maps in the wild: revealing the use of metric information in black howler monkey route navigation

When navigating, wild animals rely on internal representations of the external world – called ‘cognitive maps’ – to take movement decisions. Generally, flexible navigation is hypothesized to be supported by sophisticated spatial skills (i.e. Euclidean cognitive maps); however, constrained movements along habitual routes are the most commonly reported navigation strategy. Even though incorporating metric information (i.e. distances and angles between locations) in route-based cognitive maps would likely enhance an animal's navigation efficiency, there has been no evidence of this strategy reported for non-human animals to date. Here, we examined the properties of the cognitive map used by a wild population of primates by testing a series of cognitive hypotheses against spatially explicit movement simulations. We collected 3104 h of ranging and behavioural data on five groups of black howler monkeys (Alouatta pigra) at Palenque National Park, Mexico, from September 2016 through August 2017. We simulated correlated random walks mimicking the ranging behaviour of the study subjects and tested for differences between observed and simulated movement patterns. Our results indicated that black howler monkeys engaged in constrained movement patterns characterized by a high path recursion tendency, which limited their capacity to travel in straight lines and approach feeding trees from multiple directions. In addition, we found that the structure of observed route networks was more complex and efficient than simulated route networks, suggesting that black howler monkeys incorporate metric information into their cognitive map. Our findings not only expand the use of metric information during route navigation to non-human animals, but also highlight the importance of considering efficient route-based navigation as a cognitively demanding mechanism.

Highlighted Article: Black howler monkeys rely on route-based cognitive maps, which constrain their movement decisions, but likely incorporate metric information to navigate more efficiently along frequently used routes.

A role for lakes in revealing the nature of animal movement using high dimensional telemetry systems

Movement ecology is increasingly relying on experimental approaches and hypothesis testing to reveal how, when, where, why, and which animals move. Movement of megafauna is inherently interesting but many of the fundamental questions of movement ecology can be efficiently tested in study systems with high degrees of control. Lakes can be seen as microcosms for studying ecological processes and the use of high-resolution positioning systems to triangulate exact coordinates of fish, along with sensors that relay information about depth, temperature, acceleration, predation, and more, can be used to answer some of movement ecology's most pressing questions. We describe how key questions in animal movement have been approached and how experiments can be designed to gather information about movement processes to answer questions about the physiological, genetic, and environmental drivers of movement using lakes. We submit that whole lake telemetry studies have a key role to play not only in movement ecology but more broadly in biology as key scientific arenas for knowledge advancement. New hardware for tracking aquatic animals and statistical tools for understanding the processes underlying detection data will continue to advance the potential for revealing the paradigms that govern movement and biological phenomena not just within lakes but in other realms spanning lands and oceans.

On the move: sloths and their epibionts as model mobile ecosystems

Sloths are unusual mobile ecosystems, containing a high diversity of epibionts living and growing in their fur as they climb slowly through the canopies of tropical forests. These epibionts include poorly studied algae, arthropods, fungi, and bacteria, making sloths likely reservoirs of unexplored biodiversity. This review aims to identify gaps and eliminate misconceptions in our knowledge of sloths and their epibionts, and to identify key questions to stimulate future research into the functions and roles of sloths within a broader ecological and evolutionary context. This review also seeks to position the sloth fur ecosystem as a model for addressing fundamental questions in metacommunity and movement ecology. The conceptual and evidence-based foundation of this review aims to serve as a guide for future hypothesis-driven research into sloths, their microbiota, sloth health and conservation, and the coevolution of symbioses in general.

The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

By shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. To address this gap, we propose a modelling framework inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animal habitats, and highlight the need for a bottom–up generative model that can depict realistic variations in habitat potential connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we lay out three key research questions and demonstrate how our framework can address them. By simulating the spread of a pathogen within a population, we show how transmission properties can be impacted by both local potential connectivity and landscape-level characteristics of habitats. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

Analysing detection gaps in acoustic telemetry data to infer differential movement patterns in fish

A wide array of technologies are available for gaining insight into the movement of wild aquatic animals. Although acoustic telemetry can lack the fine-scale spatial resolution of some satellite tracking technologies, the substantially longer battery life can yield important long-term data on individual behavior and movement for low per-unit cost. Typically, however, receiver arrays are designed to maximize spatial coverage at the cost of positional accuracy leading to potentially longer detection gaps as individuals move out of range between monitored locations. This is particularly true when these technologies are deployed to monitor species in hard-to-access locations.Here, we develop a novel approach to analyzing acoustic telemetry data, using the timing and duration of gaps between animal detections to infer different behaviors. Using the durations between detections at the same and different receiver locations (i.e., detection gaps), we classify behaviors into "restricted" or potential wider "out-of-range" movements synonymous with longer distance dispersal. We apply this method to investigate spatial and temporal segregation of inferred movement patterns in two sympatric species of reef shark within a large, remote, marine protected area (MPA). Response variables were generated using network analysis, and drivers of these movements were identified using generalized linear mixed models and multimodel inference.Species, diel period, and season were significant predictors of "out-of-range" movements. Silvertip sharks were overall more likely to undertake "out-of-range" movements, compared with gray reef sharks, indicating spatial segregation, and corroborating previous stable isotope work between these two species. High individual variability in "out-of-range" movements in both species was also identified.We present a novel gap analysis of telemetry data to help infer differential movement and space use patterns where acoustic coverage is imperfect and other tracking methods are impractical at scale. In remote locations, inference may be the best available tool and this approach shows that acoustic telemetry gap analysis can be used for comparative studies in fish ecology, or combined with other research techniques to better understand functional mechanisms driving behavior.

Multiple spatial behaviours govern social network positions in a wild ungulate

The structure of wild animal social systems depends on a complex combination of intrinsic and extrinsic drivers. Population structuring and spatial behaviour are key determinants of individuals' observed social behaviour, but quantifying these spatial components alongside multiple other drivers remains difficult due to data scarcity and analytical complexity. We used a 43-year dataset detailing a wild red deer population to investigate how individuals' spatial behaviours drive social network positioning, while simultaneously assessing other potential contributing factors. Using Integrated Nested Laplace Approximation (INLA) multi-matrix animal models, we demonstrate that social network positions are shaped by two-dimensional landscape locations, pairwise space sharing, individual range size, and spatial and temporal variation in population density, alongside smaller but detectable impacts of a selection of individual-level phenotypic traits. These results indicate strong, multifaceted spatiotemporal structuring in this society, emphasising the importance of considering multiple spatial components when investigating the causes and consequences of sociality.

Social Network Analysis in Farm Animals: Sensor-Based Approaches

Simple Summary

Social behaviour of farm animals significantly impacts management interventions in the livestock sector and, thereby, animal welfare. Evaluation and monitoring of social networks between farm animals help not only to understand the bonding and agonistic behaviours among individuals but also the interactions between the animals and the animal caretaker. The interrelationship between social and environmental conditions, and the subtle changes in the behaviours of farm animals can be understood and precisely measured only by using sensing technologies. This review aims to highlight the use of sensing technologies in the investigation of social network analysis of farm animals.

Abstract

Natural social systems within animal groups are an essential aspect of agricultural optimization and livestock management strategy. Assessing elements of animal behaviour under domesticated conditions in comparison to natural behaviours found in wild settings has the potential to address issues of animal welfare effectively, such as focusing on reproduction and production success. This review discusses and evaluates to what extent social network analysis (SNA) can be incorporated with sensor-based data collection methods, and what impact the results may have concerning welfare assessment and future farm management processes. The effectiveness and critical features of automated sensor-based technologies deployed in farms include tools for measuring animal social group interactions and the monitoring and recording of farm animal behaviour using SNA. Comparative analyses between the quality of sensor-collected data and traditional observational methods provide an enhanced understanding of the behavioural dynamics of farm animals. The effectiveness of sensor-based approaches in data collection for farm animal behaviour measurement offers unique opportunities for social network research. Sensor-enabled data in livestock SNA addresses the biological aspects of animal behaviour via remote real-time data collection, and the results both directly and indirectly influence welfare assessments, and farm management processes. Finally, we conclude with potential implications of SNA on modern animal farming for improvement of animal welfare.

Characterizing the landscape of movement to identify critical wildlife habitat and corridors

Landscape planning that ensures the ecological integrity of ecosystems is critical in the face of rapid human-driven habitat conversion and development pressure. Wildlife tracking data provide unique and valuable information on animal distribution and location-specific behaviors that can serve to increase the efficacy of such planning. Given the spatiotemporal complexity inherent to animal movements, the interaction between movement behavior and a location is often oversimplified in commonly applied analyses of tracking data. We analyzed GPS-tracking-derived metrics of intensity of use, structural properties (based on network theory), and properties of the movement path (speed and directionality) with machine learning to define homogeneous spatial movement types. We applied our approach to a long-term tracking data set of over 130 African elephants (Loxodonta africana) in an area under pressure from infrastructure development. We identified 5 unique location-specific movement categories displayed by elephants, generally defined as high, medium, and low use intensity, and 2 types of connectivity corridors associated with fast and slow movements. High-use and slow-movement corridors were associated with similar landscape characteristics associated with productive areas near water, whereas low-use and fast corridors were characterized by areas of low productivity farther from water. By combining information on intensity of use, properties of movement paths, and structural aspects of movement across the landscape, our approach provides an explicit definition of the functional role of areas for movement across the landscape that we term the movescape. This combined, high-resolution information regarding wildlife space use offers mechanistic information that can improve landscape planning.

Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data

In the 4.5 decades since Altmann (1974) published her seminal paper on the methods for the observational study of behaviour, automated detection and analysis of social interaction networks have fundamentally transformed the ways that ecologists study social behaviour. Methodological developments for collecting data remotely on social behaviour involve indirect inference of associations, direct recordings of interactions and machine vision. These recent technological advances are improving the scale and resolution with which we can dissect interactions among animals. They are also revealing new intricacies of animal social interactions at spatial and temporal resolutions as well as in ecological contexts that have been hidden from humans, making the unwatchable seeable. We first outline how these technological applications are permitting researchers to collect exquisitely detailed information with little observer bias. We further recognize new emerging challenges from these new reality-mining approaches. While technological advances in automating data collection and its analysis are moving at an unprecedented rate, we urge ecologists to thoughtfully combine these new tools with classic behavioural and ecological monitoring methods to place our understanding of animal social networks within fundamental biological contexts.

Integrating social behaviour, demography and disease dynamics in network models: applications to disease management in declining wildlife populations

The emergence and spread of infections can contribute to the decline and extinction of populations, particularly in conjunction with anthropogenic environmental change. The importance of heterogeneity in processes of transmission, resistance and tolerance is increasingly well understood in theory, but empirical studies that consider both the demographic and behavioural implications of infection are scarce. Non-random mixing of host individuals can impact the demographic thresholds that determine the amplification or attenuation of disease prevalence. Risk assessment and management of disease in threatened wildlife populations must therefore consider not just host density, but also the social structure of host populations. Here we integrate the most recent developments in epidemiological research from a demographic and social network perspective, and synthesize the latest developments in social network modelling for wildlife disease, to explore their applications to disease management in populations in decline and at risk of extinction. We use simulated examples to support our key points and reveal how disease-management strategies can and should exploit both behavioural and demographic information to prevent or control the spread of disease. Our synthesis highlights the importance of considering the combined impacts of demographic and behavioural processes in epidemics to successful disease management in a conservation context. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

Sea surface temperature dictates movement and habitat connectivity of Atlantic cod in a coastal fjord system

While movements of organisms have been studied across a myriad of environments, information is often lacking regarding spatio-seasonal patterning in complex temperate coastal systems. Highly mobile fish form an integral part of marine food webs providing linkages within and among habitats, between patches of habitats, and at different life stages. We investigated how movement, activity, and connectivity patterns of Atlantic cod (Gadus morhua) are influenced by dynamic environmental conditions. Movement patterns of 39 juvenile and subadult Atlantic cod were assessed in two coastal sites in the Swedish Skagerrak for 5 months. We used passive acoustic telemetry and network analysis to assess seasonal and spatial movement patterns of cod and their relationships to different environmental factors, using statistical correlations, analysis of recurrent spatial motifs, and generalized linear mixed models. Temperature, in combination with physical barriers, precludes significant connectivity (complex motifs) within the system. Sea surface temperature had a strong influence on connectivity (node strength, degree, and motif frequency), where changes from warmer summer waters to colder winter waters significantly reduced movement activity of fish. As the seasons changed, movement of fish gradually decreased from large-scale (km) linkages in the summer to more localized movement patterns in the winter (limited to 100s m). Certain localized areas, however, were identified as important for connectivity throughout the whole study period, likely due to these multiple-habitat areas fulfilling functions required for foraging and shelter. This study provides new knowledge regarding inshore movement dynamics of juvenile and subadult Atlantic cod that use complex, coastal fjord systems. The findings show that connectivity, seasonal patterns in particular, should be carefully considered when selecting conservation areas to promote marine stewardship.

Spatially explicit network analysis reveals multi-species annual cycle movement patterns of sea ducks

Conservation of long-distance migratory species poses unique challenges. Migratory connectivity, that is, the extent to which groupings of individuals at breeding sites are maintained in wintering areas, is frequently used to evaluate population structure and assess use of key habitat areas. However, for species with complex or variable annual cycle movements, this traditional bimodal framework of migratory connectivity may be overly simplistic. Like many other waterfowl, sea ducks often travel to specific pre- and post-breeding sites outside their nesting and wintering areas to prepare for migration by feeding extensively and, in some cases, molting their flight feathers. These additional migrations may play a key role in population structure, but are not included in traditional models of migratory connectivity. Network analysis, which applies graph theory to assess linkages between discrete locations or entities, offers a powerful tool for quantitatively assessing the contributions of different sites used throughout the annual cycle to complex spatial networks. We collected satellite telemetry data on annual cycle movements of 672 individual sea ducks of five species from throughout eastern North America and the Great Lakes. From these data, we constructed a multi-species network model of migratory patterns and site use over the course of breeding, molting, wintering, and migratory staging. Our results highlight inter- and intra-specific differences in the patterns and complexity of annual cycle movement patterns, including the central importance of staging and molting sites in James Bay, the St. Lawrence River, and southern New England to multi-species annual cycle habitat linkages, and highlight the value of Long-tailed Ducks (Calengula haemalis) as an umbrella species to represent the movement patterns of multiple sea duck species. We also discuss potential applications of network migration models to conservation prioritization, identification of population units, and integrating different data streams.

Mobile marine predators: an understudied source of nutrients to coral reefs in an unfished atoll

Animal movements can facilitate important ecological processes, and wide-ranging marine predators, such as sharks, potentially contribute significantly towards nutrient transfer between habitats. We applied network theory to 4 years of acoustic telemetry data for grey reef sharks (Carcharhinus amblyrhynchos) at Palmyra, an unfished atoll, to assess their potential role in nutrient dynamics throughout this remote ecosystem. We evaluated the dynamics of habitat connectivity and used network metrics to quantify shark-mediated nutrient distribution. Predator movements were consistent within year, but differed between years and by sex. Females used higher numbers of routes throughout the system, distributing nutrients over a larger proportion of the atoll. Extrapolations of tagged sharks to the population level suggest that prey consumption and subsequent egestion leads to the heterogeneous deposition of 94.5 kg d^-1 of nitrogen around the atoll, with approximately 86% of this probably derived from pelagic resources. These results suggest that sharks may contribute substantially to nutrient transfer from offshore waters to near-shore reefs, subsidies that are important for coral reef health.

Assessing Behavioral Associations in a Hybrid Zone through Social Network Analysis: Complex Assortative Behaviors Structure Associations in a Hybrid Quail Population

Behavior can strongly influence rates and patterns of hybridization between animal populations and species. Yet few studies have examined reproductive behaviors in natural hybrid zones within the fine-scale social context in which they naturally occur. We use radio-frequency identification tags with social network analyses to test whether phenotypic similarity in plumage and mass correlate with social behavior throughout a breeding season in a California and Gambel's quail hybrid zone. We use a novel approach to partition phenotypic variation in a way that does not confound differences between sexes and species, and we illustrate the complex ways that phenotype and behavior structure the social environment, mating opportunities, and male-male associations. Associations within the admixed population were random with respect to species-specific plumage but showed strong patterns of assortment based on sexually dimorphic plumage, monomorphic plumage, and mass. Weak behavioral reproductive isolation in this admixed population may be the result of complex patterns of phenotypic assortment based on multiple traits rather than a lack of phenotypic discrimination. More generally, our results support the utility of social network analyses for analyzing behavioral factors affecting genetic exchange between populations and species.