A synthesis of health benefits of natural sounds and their distribution in national parks.

Parks are important places to listen to natural sounds and avoid human-related noise, an increasingly rare combination. We first explore whether and to what degree natural sounds influence health outcomes using a systematic literature review and meta-analysis. We identified 36 publications examining the health benefits of natural sound. Meta-analyses of 18 of these publications revealed aggregate evidence for decreased stress and annoyance (g = -0.60, 95% CI = -0.97, -0.23) and improved health and positive affective outcomes (g = 1.63, 95% CI = 0.09, 3.16). Examples of beneficial outcomes include decreased pain, lower stress, improved mood, and enhanced cognitive performance. Given this evidence, and to facilitate incorporating public health in US national park soundscape management, we then examined the distribution of natural sounds in relation to anthropogenic sound at 221 sites across 68 parks. National park soundscapes with little anthropogenic sound and abundant natural sounds occurred at 11.3% of the sites. Parks with high visitation and urban park sites had more anthropogenic sound, yet natural sounds associated with health benefits also were frequent. These included animal sounds (audible for a mean of 59.3% of the time, SD: 23.8) and sounds from wind and water (mean: 19.2%, SD: 14.8). Urban and other parks that are extensively visited offer important opportunities to experience natural sounds and are significant targets for soundscape conservation to bolster health for visitors. Our results assert that natural sounds provide important ecosystem services, and parks can bolster public health by highlighting and conserving natural soundscapes.

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.

Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace-of-life.

Pathogen transmission depends on host density, mobility and contact. These components emerge from host and pathogen movements that themselves arise through interactions with the surrounding environment. The environment, the emergent host and pathogen movements, and the subsequent patterns of density, mobility and contact form an 'epidemiological landscape' connecting the environment to specific locations where transmissions occur. Conventionally, the epidemiological landscape has been described in terms of the geographical coordinates where hosts or pathogens are located. We advocate for an alternative approach that relates those locations to attributes of the local environment. Environmental descriptions can strengthen epidemiological forecasts by allowing for predictions even when local geographical data are not available. Environmental predictions are more accessible than ever thanks to new tools from movement ecology, and we introduce a 'movement-pathogen pace of life' heuristic to help identify aspects of movement that have the most influence on spatial epidemiology. By linking pathogen transmission directly to the environment, the epidemiological landscape offers an efficient path for using environmental information to inform models describing when and where transmission will occur.

Genomic correlates for migratory direction in a free-ranging cervid.

Animal migrations are some of the most ubiquitous and one of the most threatened ecological processes globally. A wide range of migratory behaviours occur in nature, and this behaviour is not uniform among and within species, where even individuals in the same population can exhibit differences. While the environment largely drives migratory behaviour, it is necessary to understand the genetic mechanisms influencing migration to elucidate the potential of migratory species to cope with novel conditions and adapt to environmental change. In this study, we identified genes associated with a migratory trait by undertaking pooled genome-wide scans on a natural population of migrating mule deer. We identified genomic regions associated with variation in migratory direction, including FITM1, a gene linked to the formation of lipids, and DPPA3, a gene linked to epigenetic modifications of the maternal line. Such a genetic basis for a migratory trait contributes to the adaptive potential of the species and might affect the flexibility of individuals to change their behaviour in the face of changes in their environment.

Socio-ecological drivers of public conservation voting: Restoring gray wolves to Colorado, USA.

Understanding factors that influence real-world public conservation behaviors is critical for developing successful conservation policies and management actions. Citizens of Colorado, USA recently passed a ballot initiative to restore the gray wolf to its former range within the state. The >3 million votes offer an unprecedented opportunity to test factors that influenced decisions to support or oppose this conservation action. We created spatial linear regression models to assess the relationship between support for wolf restoration and (1) the presidential vote, (2) distance to conservation intervention (i.e., proposed wolf reintroduction and existing wolves), and measures of (3) livelihood and (4) demographics using precinct-level data. Our results demonstrate the strong relationship between support for wolf restoration and political support for the Democratic candidate for president in the 2020 election, and highlight how other factors, including increased age, participation in elk hunting, and proximity to the reintroduction region were associated with less support. Our findings underscore the critical role of politicization on public conservation action and the need to develop outreach and engagement strategies to mitigate polarization.

Accounting for animal movement improves vaccination strategies against wildlife disease in heterogeneous landscapes.

Oral baiting is used to deliver vaccines to wildlife to prevent, control, and eliminate infectious diseases. A central challenge is how to spatially distribute baits to maximize encounters by target animal populations, particularly in urban and suburban areas where wildlife such as raccoons (Procyon lotor) are abundant and baits are delivered along roads. Methods from movement ecology that quantify movement and habitat selection could help to optimize baiting strategies by more effectively targeting wildlife populations across space. We developed a spatially explicit, individual-based model of raccoon movement and oral rabies vaccine seroconversion to examine whether and when baiting strategies that match raccoon movement patterns perform better than currently used baiting strategies in an oral rabies vaccination zone in greater Burlington, Vermont, USA. Habitat selection patterns estimated from locally radio-collared raccoons were used to parameterize movement simulations. We then used our simulations to estimate raccoon population rabies seroprevalence under currently used baiting strategies (actual baiting) relative to habitat selection-based baiting strategies (habitat baiting). We conducted simulations on the Burlington landscape and artificial landscapes that varied in heterogeneity relative to Burlington in the proportion and patch size of preferred habitats. We found that the benefits of habitat baiting strongly depended on the magnitude and variability of raccoon habitat selection and the degree of landscape heterogeneity within the baiting area. Habitat baiting improved seroprevalence over actual baiting for raccoons characterized as habitat specialists but not for raccoons that displayed weak habitat selection similar to radiocollared individuals, except when baits were delivered off roads where preferred habitat coverage and complexity was more pronounced. In contrast, in artificial landscapes with either more strongly juxtaposed favored habitats and/or higher proportions of favored habitats, habitat baiting performed better than actual baiting, even when raccoons displayed weak habitat preferences and where baiting was constrained to roads. Our results suggest that habitat selection-based baiting could increase raccoon population seroprevalence in urban-suburban areas, where practical, given the heterogeneity and availability of preferred habitat types in those areas. Our novel simulation approach provides a flexible framework to test alternative baiting strategies in multiclass landscapes to optimize bait-distribution strategies.

Simple metrics to characterize inter-individual and temporal variation in habitat selection behaviour.

Individual variation in habitat selection and movement behaviour is receiving growing attention, but primarily with respect to characterizing behaviours in different contexts as opposed to decomposing structure in behaviour within populations. This focus may be limiting advances in understanding the diversity of individual behaviour and its influence on population organization. We propose a framework for characterizing variation in space-use behaviour with the aim of advancing interpretation of its form and function. Using outputs from integrated step-selection analyses of 20 years of telemetry data from African elephants Loxodonta Africana, we developed four metrics characterizing differentiation in resource selection behaviour within a population (specialization [magnitude of the response independent of direction], heterogeneity [inter-individual variation], consistency [temporal shift in response] and reversal [frequency of directional changes in the response]). We contrasted insight from the developed metrics relative to the mean population response using an example focused on two covariates. We then expanded this contrast by evaluating if the metrics identify structurally important information on seasonal shifts in resource selection behaviours in addition to that provided by mean selection coefficients through principal component analyses (PCAs) and a random forest classification. The simplified example highlighted that for some covariates focusing on the population average failed to capture complex individual variation in behaviours. The PCAs revealed that the developed metrics provided additional information in explaining the patterns in elephant selection beyond that offered by population average covariate values. For elephants, specialization and heterogeneity were informative, with specialization often being a better descriptor of differences in seasonal resource selection behaviour than population average responses. Summarizing these metrics spatially and temporally, we illustrate how these metrics can provide insights on overlooked aspects of animal behaviour. Our work offers a new approach in how we conceptualize variation in space-use behaviour (i.e. habitat selection and movement) by providing ways of encapsulating variation that enables diagnoses of the drivers of individual-level variability in a population. The developed metrics explicitly distil how variation in a behaviour is structured among individuals and over time which could facilitate comparative work across time, populations or strata within populations.

Risk perception and tolerance shape variation in agricultural use for a transboundary elephant population.

To conserve wide-ranging species in human-modified landscapes, it is essential to understand how animals selectively use or avoid cultivated areas. Use of agriculture leads to human-wildlife conflict, but evidence suggests that individuals may differ in their tendency to be involved in conflict. This is particularly relevant to wild elephant populations. We analysed GPS data of 66 free-ranging elephants in the Serengeti-Mara ecosystem to quantify their use of agriculture. We then examined factors influencing the level of agricultural use, individual change in use across years and differences in activity budgets associated with use. Using clustering methods, our data grouped into four agricultural use tactics: rare (<0.6% time in agriculture; 26% of population), sporadic (0.6%-3.8%; 34%), seasonal (3.9%-12.8%; 31%) and habitual (>12.8%; 9%). Sporadic and seasonal individuals represented two-thirds (67%) of recorded GPS fixes in agriculture, compared to 32% from habitual individuals. Increased agricultural use was associated with higher daily distance travelled and larger home range size, but not with age or sex. Individual tactic change was prevalent and the habitual tactic was maintained in consecutive years by only five elephants. Across tactics, individuals switched from diurnal to nocturnal activity during agricultural use, interpreted as representing similar risk perception of cultivated areas. Conversely, tactic choice appeared to be associated with differences in risk tolerance between individuals. Together, our results suggest that elephants are balancing the costs and benefits of crop usage at both fine (e.g. crop raid events) and long (e.g. yearly tactic change) temporal scales. The high proportion of sporadic and seasonal tactics also highlights the importance of mitigation strategies that address conflict arising from many animals, rather than targeted management of habitual crop raiders. Our approach can be applied to other species and systems to characterize individual variation in human resource use and inform mitigations for human-wildlife coexistence.

A global community-sourced assessment of the state of conservation technology.

Conservation technology holds the potential to vastly increase conservationists' ability to understand and address critical environmental challenges, but systemic constraints appear to hamper its development and adoption. Understanding of these constraints and opportunities for advancement remains limited. We conducted a global online survey of 248 conservation technology users and developers to identify perceptions of existing tools' current performance and potential impact, user and developer constraints, and key opportunities for growth. We also conducted focus groups with 45 leading experts to triangulate findings. The technologies with the highest perceived potential were machine learning and computer vision, eDNA and genomics, and networked sensors. A total of 95%, 94%, and 92% respondents, respectively, rated them as very helpful or game changers. The most pressing challenges affecting the field as a whole were competition for limited funding, duplication of efforts, and inadequate capacity building. A total of 76%, 67%, and 55% respondents, respectively, identified these as primary concerns. The key opportunities for growth identified in focus groups were increasing collaboration and information sharing, improving the interoperability of tools, and enhancing capacity for data analyses at scale. Some constraints appeared to disproportionately affect marginalized groups. Respondents in countries with developing economies were more likely to report being constrained by upfront costs, maintenance costs, and development funding (p = 0.048, odds ratio [OR] = 2.78; p = 0.005, OR = 4.23; p = 0.024, OR = 4.26), and female respondents were more likely to report being constrained by development funding and perceived technical skills (p = 0.027, OR = 3.98; p = 0.048, OR = 2.33). To our knowledge, this is the first attempt to formally capture the perspectives and needs of the global conservation technology community, providing foundational data that can serve as a benchmark to measure progress. We see tremendous potential for this community to further the vision they define, in which collaboration trumps competition; solutions are open, accessible, and interoperable; and user-friendly processing tools empower the rapid translation of data into conservation action. Article impact statement: Addressing financing, coordination, and capacity-building constraints is critical to the development and adoption of conservation technology.

La tecnología de conservación tiene el potencial para incrementar considerablemente la habilidad de los conservacionistas para entender y lidiar con los retos ambientales más importantes, pero las restricciones sistémicas parecen dificultar su desarrollo y adopción. La comprensión de estas restricciones y las oportunidades para el avance todavía son limitadas. Encuestamos en línea a 248 usuarios y programadores mundiales de tecnología de conservación para identificar las percepciones existentes del desempeño e impacto potencial de las herramientas actuales, restricciones para los usuarios y programadores y oportunidades clave para el crecimiento. También realizamos grupos de discusión con 45 expertos destacados para triangular los hallazgos. Las tecnologías con el potencial percibido más alto fueron el aprendizaje mecánico y la visión por computadora, la genómica y el eADN y los sensores en red. El 95%, 94% y 92% de los respondientes, respectivamente, clasificó estas tecnologías como muy útiles o como puntos de inflexión. Los retos más apremiantes que afectaron al área como conjunto fueron la competencia por el financiamiento limitado, la duplicación de esfuerzos y el desarrollo inadecuado de capacidades. El 76%, 67% y 55% de los respondientes, respectivamente, identificaron estos retos como de interés primario. Las oportunidades clave para el crecimiento que se identificaron en los grupos de diálogo fueron el incremento de la colaboración y la distribución de información, la mejoría de la operatividad entre herramientas y la potenciación de la capacidad de análisis de datos a escala. Algunas restricciones parecieron afectar desproporcionadamente a grupos marginalizados. Los respondientes de países con economías en desarrollo tuvieron mayor probabilidad de reportar la restricción por los costos iniciales, costos de mantenimiento y la financiación del desarrollo (p = 0.048, tasa de probabilidad [OR] = 2.78; p = 0.005, OR = 4.23; p = 0.024, OR = 4.26), y las mujeres respondientes tuvieron una mayor probabilidad de reportar restricciones por la financiación del desarrollo y habilidades técnicas percibidas (p = 0.027, OR = 3.98; p = 0.048, OR = 2.33). A nuestro entendimiento, este es el primero intento por capturar formalmente las perspectivas y necesidades de la comunidad mundial de la tecnología de conservación, proporcionando datos fundamentales que pueden servir como referencia para medir el progreso. Vemos un potencial tremendo para que esta comunidad amplíe la visión que definen, en la cual la colaboración se sobrepone a la competencia; las soluciones son abierta, accesibles e interoperativas; y las herramientas intuitivas de procesamiento capacitan la traducción veloz de datos a acciones de conservación.

Disturbance type and species life history predict mammal responses to humans.

Human activity and land use change impact every landscape on Earth, driving declines in many animal species while benefiting others. Species ecological and life history traits may predict success in human-dominated landscapes such that only species with "winning" combinations of traits will persist in disturbed environments. However, this link between species traits and successful coexistence with humans remains obscured by the complexity of anthropogenic disturbances and variability among study systems. We compiled detection data for 24 mammal species from 61 populations across North America to quantify the effects of (1) the direct presence of people and (2) the human footprint (landscape modification) on mammal occurrence and activity levels. Thirty-three percent of mammal species exhibited a net negative response (i.e., reduced occurrence or activity) to increasing human presence and/or footprint across populations, whereas 58% of species were positively associated with increasing disturbance. However, apparent benefits of human presence and footprint tended to decrease or disappear at higher disturbance levels, indicative of thresholds in mammal species' capacity to tolerate disturbance or exploit human-dominated landscapes. Species ecological and life history traits were strong predictors of their responses to human footprint, with increasing footprint favoring smaller, less carnivorous, faster-reproducing species. The positive and negative effects of human presence were distributed more randomly with respect to species trait values, with apparent winners and losers across a range of body sizes and dietary guilds. Differential responses by some species to human presence and human footprint highlight the importance of considering these two forms of human disturbance separately when estimating anthropogenic impacts on wildlife. Our approach provides insights into the complex mechanisms through which human activities shape mammal communities globally, revealing the drivers of the loss of larger predators in human-modified landscapes.

Noise and landscape features influence habitat use of mammalian herbivores in a natural gas field.

Anthropogenic noise is a complex disturbance known to elicit a variety of responses in wild animals. Most studies examining the effects of noise on wildlife focus on vocal species, although theory suggests that the acoustic environment influences non-vocal species as well. Common mammalian prey species, like mule deer and hares and rabbits (members of the family Leporidae), rely on acoustic cues for information regarding predation, but the impacts of noise on their behaviour has received little attention. We paired acoustic recorders with camera traps to explore how average daily levels of anthropogenic noise from natural gas activity impacted occupancy and detection of mammalian herbivores in an energy field in the production phase of development. We consider the effects of noise in the context of several physical landscape variables associated with natural gas infrastructure that are known to influence habitat use patterns in mule deer. Our results suggest that mule deer detection probability was influenced by the interaction between physical landscape features and anthropogenic noise, with noise strongly reducing habitat use. In contrast, leporid habitat use was not related to noise but was influenced by landscape features. Notably, mule deer showed a stronger predicted negative response to roads with high noise exposure. This study highlights the complex interactions of anthropogenic disturbance and wildlife distribution and presents important evidence that the effects of anthropogenic noise should be considered in research focused on non-vocal specialist species and management plans for mule deer and other large ungulates.

El ruido de origen antropogénico es una perturbación compleja que provoca una variedad de respuestas en la fauna silvestre. La mayoría de los estudios que examina los efectos del ruido en fauna silvestre se enfoca en especies que se comunican con vocalizaciones, sin embargo, la teoría sugiere que el ambiente acústico es también un recurso clave para especies no-vocales. Especies de mamíferos comunes como el venado bura, liebres y conejos (miembros de la familia Leporidae), dependen de señales acústicas para detectar depredadores, pero los impactos del ruido en el comportamiento de estas especies han recibido poca atención de los investigadores. Usando grabadoras y cámaras trampa en conjunto, exploramos como los niveles diarios de ruido antropogénico generados por la extracción de gas natural impactaron la ocupación y detección de mamíferos herbívoros en un campo de extracción de energía en la fase de producción de desarrollo. Consideramos los efectos del ruido en el contexto de varias variables físicas del paisaje asociadas con la infraestructura del gas natural, que sabemos, influencian los patrones de uso de hábitat del venado bura. Nuestros resultados sugieren que la probabilidad de detección del venado bura fue influenciada por la interacción de las características físicas del paisaje y el ruido antropogénico, este último reduciendo de manera importante el uso de hábitat. En contraste, el uso de hábitat de lepóridos no se relacionó con ruido, pero fue influenciado por variables del paisaje. Notablemente, el venado bura mostro una predicción de respuesta fuerte a los caminos con niveles altos de ruido. Este estudio señala las complejas interacciones entre perturbaciones antropogénicas y la distribución de fauna silvestre. También presentamos evidencia importante que señala que los efectos del ruido antropogénico deben ser considerados en investigaciones enfocadas en especies especialistas no-vocales y planes de manejo para el venado bura y otros ungulados grandes.

Effects of social structure and management on risk of disease establishment in wild pigs.

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group and spatial distribution (monthly home-range overlap and distance) on wild pig contact. We also estimated the impacts of management-induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between-group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modelling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low-intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. We demonstrate an approach for understanding the relative role of spatial, social and individual-level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially and socially structured wildlife species.

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.

Caracterización del Paisaje de Movimiento para Identificar Hábitats y Corredores de Fauna Importantes Resumen La planeación de paisajes que asegura la integridad ecológica de los ecosistemas es muy importante de cara a la rápida conversión de hábitats llevada por la acción humana y la presión del desarrollo. Los datos de rastreo de fauna proporcionan información única y valiosa sobre la distribución animal y el comportamiento específico por localidad que puede servir para incrementar la eficiencia de dicha planeación. Dada la complejidad espaciotemporal inherente al movimiento animal, la interacción entre la conducta de movimiento y la ubicación con frecuencia se ve sobre simplificada en los análisis de información de rastreos aplicados comúnmente. Analizamos las medidas derivadas de rastreos por GPS de la intensidad de uso, las propiedades estructurales (basadas en la teoría de redes) y las propiedades de la vía de movimiento (velocidad y direccionalidad) con aprendizaje automatizado para definir los tipos de movimiento espacial homogéneo. Aplicamos nuestra estrategia a un conjunto de datos de rastreo a largo plazo de más de 130 elefantes africanos (Loxodonta africana) en un área bajo presión ocasionada por el desarrollo de infraestructura. Identificamos cinco categorías de movimiento específico por localidad exhibidas por los elefantes, definidas en términos generales como intensidad de uso alta, media y baja. También identificamos dos tipos de corredores de conectividad asociados con movimientos rápidos y lentos. Los corredores de intensidad de uso alta y movimiento lento estuvieron asociados con las características similares de paisaje asociadas a las áreas productivas cercanas a cuerpos de agua, mientras que los corredores de intensidad baja y movimiento rápido estuvieron caracterizados por áreas de baja productividad alejadas de los cuerpos de agua. Con la combinación de la información sobre la intensidad de uso, las propiedades de las vías de movimiento y los aspectos estructurales del movimiento a lo largo del paisaje, nuestra estrategia proporciona una definición explícita del papel funcional que tienen las áreas de movimiento en el paisaje, la cual denominamos paisaje de movimiento (movescape). Esta información combinada y de alta resolución con respecto al uso espacial por la fauna ofrece información mecánica que puede mejorar la planeación del paisaje.

Movement reveals reproductive tactics in male elephants.

Long-term bio-logging has the potential to reveal how movements, and hence life-history trade-offs, vary over a lifetime. Reproductive tactics in particular may vary as individuals' trade-off current investment versus lifetime fitness. Male African savanna elephants (Loxodona africana) provide a telling example of balancing body growth with reproductive fitness due to the combination of indeterminate growth and strongly delineated periods of sexual activity (musth), which results in reproductive tactics that alter with age. Our study aims to quantify the extent to which male elephants alter their movement patterns, and hence energetic allocation, in relation to (a) reproductive state and (b) age, and (c) to determine whether musth periods can be detected directly from GPS tracking data. We used a combination of GPS tracking data and visual observations of 25 male elephants ranging in age from 20 to 52 years to examine the influence of reproductive state and age on movement. We then used a three-state hidden Markov model (HMM) to detect musth behaviour in a subset of sequential tracking data. Our results demonstrate that male elephants increased their daily mean speed and range size with age and in musth. Furthermore, non-musth speed decreased with age, presumably reflecting a shift towards energy acquisition during non-musth. Thus, despite similar speeds and marginally larger ranges between reproductive states at age 20, by age 50, males were travelling 2.0 times faster in a 3.5 times larger area in musth relative to non-musth. The distinctiveness of musth periods over age 35 meant the three-state HMM could automatically detect musth movement with high sensitivity and specificity, but could not for the younger age class. We show that male elephants increased their energetic allocation into reproduction with age as the probability of reproductive success increases. Given that older male elephants tend to be both the target of legal trophy hunting and illegal poaching, man-made interference could drive fundamental changes in elephant reproductive tactics. Bio-logging, as our study reveals, has the potential both to quantify mature elephant reproductive tactics remotely and to be used to institute proactive management strategies around the reproductive behaviour of this charismatic keystone species.

Strongylid infection varies with age, sex, movement and social factors in wild African elephants.

Comparing parasitic infection among individuals of wildlife populations can provide insight into factors that influence wildlife disease ecology. Strongylids are parasitic worms that infect the intestinal tract of vertebrates, and infection with strongylids can be approximated by counting strongylid eggs in dung samples. Here we tested for correlations between strongylid egg counts and 18 different individual characteristics, environmental and social factors in individually known wild African elephants. We counted more eggs in the dung samples of younger elephants and females relative to mature elephants and males. We also found that elephants spending more time outside reserves shed more strongylid eggs than elephants that were more often within reserves. Elephants that were less socially integrated, as measured by how much aggression they received from other elephants, shed fewer strongylid eggs; relatedly, socially isolated orphan elephants that had left their family shed fewer strongylid eggs than elephants that remained with their family. Our results suggest that landscapes altered by livestock grazing and social disruption caused by humans may impact parasitic infection in wildlife.

Leveraging multidimensional heterogeneity in resource selection to define movement tactics of animals.

Increasing interest in the complexity, variation and drivers of movement-related behaviours promise new insight into fundamental components of ecology. Resolving the multidimensionality of spatially explicit behaviour remains a challenge for investigating tactics and their relation to niche construction, but high-resolution movement data are providing unprecedented understanding of the diversity of spatially explicit behaviours. We introduce a framework for investigating individual variation in movement-defined resource selection that integrates the behavioural and ecological niche concepts. We apply it to long-term tracking data of 115 African elephants (Loxodonta africana), illustrating how a behavioural hypervolume can be defined based on differences between individuals and their ecological settings, and applied to explore population heterogeneity. While normative movement behaviour is frequently used to characterise population behaviour, we demonstrate the value of leveraging heterogeneity in the behaviour to gain greater insight into population structure and the mechanisms driving space-use tactics.

Forecasting Ecological Genomics: High-Tech Animal Instrumentation Meets High-Throughput Sequencing.

Recent advancements in animal tracking technology and high-throughput sequencing are rapidly changing the questions and scope of research in the biological sciences. The integration of genomic data with high-tech animal instrumentation comes as a natural progression of traditional work in ecological genetics, and we provide a framework for linking the separate data streams from these technologies. Such a merger will elucidate the genetic basis of adaptive behaviors like migration and hibernation and advance our understanding of fundamental ecological and evolutionary processes such as pathogen transmission, population responses to environmental change, and communication in natural populations.

Inter-generational change in African elephant range use is associated with poaching risk, primary productivity and adult mortality.

Repeated use of the same areas may benefit animals as they exploit familiar sites, leading to consistent home ranges over time that can span generations. Changing risk landscapes may reduce benefits associated with home range fidelity, however, and philopatric animals may alter movement in response to new pressures. Despite the importance of range changes to ecological and evolutionary processes, little tracking data have been collected over the long-term nor has range change been recorded in response to human pressures across generations. Here, we investigate the relationships between ecological, demographic and human variables and elephant ranging behaviour across generations using 16 years of tracking data from nine distinct female social groups in a population of elephants in northern Kenya that was heavily affected by ivory poaching during the latter half of the study. Nearly all groups-including those that did not experience loss of mature adults-exhibited a shift north over time, apparently in response to increased poaching in the southern extent of the study area. However, loss of mature adults appeared to be the primary indicator of range shifts and expansions, as generational turnover was a significant predictor of range size increases and range centroid shifts. Range expansions and northward shifts were associated with higher primary productivity and lower poached carcass densities, while westward shifts exhibited a trend to areas with higher values of primary productivity and higher poached carcass densities relative to former ranges. Together these results suggest a trade-off between resource access, mobility and safety. We discuss the relevance of these results to elephant conservation efforts and directions meriting further exploration in this disrupted society of a keystone species.

Applying network theory to animal movements to identify properties of landscape space use.

Network (graph) theory is a popular analytical framework to characterize the structure and dynamics among discrete objects and is particularly effective at identifying critical hubs and patterns of connectivity. The identification of such attributes is a fundamental objective of animal movement research, yet network theory has rarely been applied directly to animal relocation data. We develop an approach that allows the analysis of movement data using network theory by defining occupied pixels as nodes and connection among these pixels as edges. We first quantify node-level (local) metrics and graph-level (system) metrics on simulated movement trajectories to assess the ability of these metrics to pull out known properties in movement paths. We then apply our framework to empirical data from African elephants (Loxodonta africana), giant Galapagos tortoises (Chelonoidis spp.), and mule deer (Odocoileous hemionus). Our results indicate that certain node-level metrics, namely degree, weight, and betweenness, perform well in capturing local patterns of space use, such as the definition of core areas and paths used for inter-patch movement. These metrics were generally applicable across data sets, indicating their robustness to assumptions structuring analysis or strategies of movement. Other metrics capture local patterns effectively, but were sensitive to specified graph properties, indicating case specific applications. Our analysis indicates that graph-level metrics are unlikely to outperform other approaches for the categorization of general movement strategies (central place foraging, migration, nomadism). By identifying critical nodes, our approach provides a robust quantitative framework to identify local properties of space use that can be used to evaluate the effect of the loss of specific nodes on range wide connectivity. Our network approach is intuitive, and can be implemented across imperfectly sampled or large-scale data sets efficiently, providing a framework for conservationists to analyze movement data. Functions created for the analyses are available within the R package moveNT.

Efficacy of extracting indices from large-scale acoustic recordings to monitor biodiversity.

Passive acoustic monitoring could be a powerful way to assess biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices (i.e., a mathematical summary of acoustic energy) offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examined the relationship between acoustic indices and the diversity and abundance of biological sounds in recordings. We reviewed the acoustic-index literature and found that over 60 indices have been applied to a range of objectives with varying success. We used 36 of the most indicative indices to develop a predictive model of the diversity of animal sounds in recordings. Acoustic data were collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental United States. For terrestrial recordings, random-forest models with a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R2  ≥ 0.94, mean squared error [MSE] ≤170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively affected by insect, weather, and anthropogenic sounds. For marine recordings, random-forest models poorly predicted Shannon diversity, richness, and total number of biological sounds (R2 ≤ 0.40, MSE ≥ 195). Our results suggest that using a combination of relevant acoustic indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats.