Memory drives the formation of animal home ranges: evidence from a reintroduction

Most animals live in home ranges, and memory is thought to be an important process in their formation. However, a general memory-based model for characterising and predicting home range emergence has been lacking. Here, we use a mechanistic movement model to: (1) quantify the role of memory in the movements of a large mammal reintroduced into a novel environment, and (2) predict observed patterns of home range emergence in this experimental setting. We show that an interplay between memory and resource preferences is the primary process influencing the movements of reintroduced roe deer (Capreolus capreolus). Our memory-based model fitted with empirical data successfully predicts the formation of home ranges, as well as emergent properties of movement and spatial revisitation observed in the reintroduced animals. These results provide a mechanistic framework for combining memory-based movements, resource preferences, and the formation of home ranges in nature

Roe deer summer habitat selection at multiple spatio-temporal scales in an alpine environment

Habitat selection is a hierarchical process that may involve different patterns depending on the spatial and temporal scales of investigation. We studied habitat selection by European roe deer (Capreolus capreolus) in a very diverse environment in the Italian eastern Alps, during summer. We sampled both coarse-grained habitat variables (topographic variables, habitat types and cover) and fine-grained habitat variables (forage components of habitat) in used and available locations along the movement trajectories of 14 adult roe deer equipped with GPS telemetry collars. We used conventional logistic regression to assess roe deer habitat selection at the seasonal home range scale, and conditional logistic regression to take into account the temporal aspect of habitat selection on a weekly basis. Our results indicate that topographic variables were not significant predictors for summer roe deer habitat selection. Roe deer strongly selected dense canopy cover, probably to avoid heat stress during warm summer days. In accordance with previous observations, roe deer preferred young forest stands dominated by pioneer species such as ash (Fraxinus spp.) and hazel (Corylus avellana) over climax environments. Roe deer positively selected shrubs (in particular Fraxinus spp., Erica herbacea, Rhododendron spp. and Vaccinium spp.) throughout the study period, whereas selection for grasses and sedges emerged only at the weekly scale. Habitat selection was clearly related to vegetation phenology, since roe deer selected plants in the most nutritive phenological stages, i.e., shrubs with buds, new leaves and fruits, and newly emergent grasses and sedges. Finally, we found stronger and more significant regression coefficients for forage components of habitat and habitat types at the weekly scale, indicating that matching spatial and temporal scales may improve our understanding of ecological patterns driving habitat selection. Conversely, selection patterns for canopy cover did not change across scales, indicating that this variable likely drives habitat selection in a similar way throughout the entire season

Roe deer summer habitat selection at multiple spatio-temporal scales in an Alpine environment

Habitat selection is a hierarchical process that may involve different patterns depending on the spatial and temporal scales of investigation. We studied habitat selection by European roe deer (Capreolus capreolus) in a very diverse environment in the Italian eastern Alps, during summer. We sampled both coarse-grained habitat variables (topographic variables, habitat types and cover) and fine-grained habitat variables (forage components of habitat) in used and available locations along the movement trajectories of 14 adult roe deer equipped with GPS telemetry collars. We used conventional logistic regression to assess roe deer habitat selection at the seasonal home range scale, and conditional logistic regression to take into account the temporal aspect of habitat selection on a weekly basis. Our results indicate that topographic variables were not significant predictors for summer roe deer habitat selection. Roe deer strongly selected dense canopy cover, probably to avoid heat stress during warm summer days. In accordance with previous observations, roe deer preferred young forest stands dominated by pioneer species such as ash (Fraxinus spp.) and hazel (Corylus avellana) over climax environments. Roe deer positively selected shrubs (in particular Fraxinus spp., Erica herbacea, Rhododendron spp. and Vaccinium spp.) throughout the study period, whereas selection for grasses and sedges emerged only at the weekly scale. Habitat selection was clearly related to vegetation phenology, since roe deer selected plants in the most nutritive phenological stages, i.e., shrubs with buds, new leaves and fruits, and newly emergent grasses and sedges. Finally, we found stronger and more significant regression coefficients for forage components of habitat and habitat types at the weekly scale, indicating that matching spatial and temporal scales may improve our understanding of ecological patterns driving habitat selection. Conversely, selection patterns for canopy cover did not change across scales, indicating that this variable likely drives habitat selection in a similar way throughout the entire season

First Record of Hepatozoon spp. in Alpine Wild Rodents: Implications and Perspectives for Transmission Dynamics across the Food Web

Among the Apicomplexa parasites, Hepatozoon spp. have been mainly studied in domestic animals and peri-urban areas. The epidemiology of Hepatozoon spp. is poorly investigated in natural systems and wild hosts because of their scarce veterinary and economic relevance. For most habitats, the occurrence of these parasites is unknown, despite their high ecosystemic role. To fill this gap for alpine small mammals, we applied molecular PCR-based methods and sequencing to determine the Hepatozoon spp. in 830 ear samples from 11 small mammal species (i.e., Apodemus, Myodes, Chionomys, Microtus, Crocidura and Sorex genera) live-trapped during a cross-sectional study along an altitudinal gradient in the North-Eastern Italian Alps. We detected Hepatozoon spp. with an overall prevalence of 35.9%. Two species ranging from 500 m a.s.l. to 2500 m a.s.l. were the most infected: My. glareolus, followed by Apodemus spp. Additionally, we detected the parasite for the first time in another alpine species: C. nivalis at 2000–2500 m a.s.l. Our findings suggest that several rodent species maintain Hepatozoon spp. along the alpine altitudinal gradient of habitats. The transmission pathway of this group of parasites and their role within the alpine mammal community need further investigation, especially in consideration of the rapidly occurring environmental and climatic changes.First Record of Hepatozoon spp. in Alpine Wild Rodents: Implications and Perspectives for Transmission Dynamics across the Food WebpublishedVersio

Investigating tritrophic interactions using bioenergetic demographic models

A central debate in ecology has been the long-running discussion on the role of apex predators in affecting the abundance and dynamics of their prey. In terrestrial systems, research has primarily relied on correlational approaches, due to the challenge of implementing robust experiments with replication and appropriate controls. A consequence of this is that we largely suffer from a lack of mechanistic understanding of the population dynamics of interacting species, which can be surprisingly complex. Mechanistic models offer an opportunity to examine the causes and consequences of some of this complexity. We present a bioenergetic mechanistic model of a tritrophic system where the primary vegetation resource follows a seasonal growth function, and the herbivore and carnivore species are modeled using two integral projection models (IPMs) with body mass as the phenotypic trait. Within each IPM, the demographic functions are structured according to bioenergetic principles, describing how animals acquire and transform resources into body mass, energy reserves, and breeding potential. We parameterize this model to reproduce the population dynamics of grass, elk, and wolves in northern Yellowstone National Park (USA) and investigate the impact of wolf reintroduction on the system. Our model generated predictions that closely matched the observed population sizes of elk and wolf in Yellowstone prior to and following wolf reintroduction. The introduction of wolves into our basal grass-elk bioenergetic model resulted in a population of 99 wolves and a reduction in elk numbers by 61% (from 14,948 to 5823) at equilibrium. In turn, vegetation biomass increased by approximately 25% in the growing season and more than threefold in the nongrowing season. The addition of wolves to the model caused the elk population to switch from being food-limited to being predator-limited and had a stabilizing effect on elk numbers across different years. Wolf predation also led to a shift in the phenotypic composition of the elk population via a small increase in elk average body mass. Our model represents a novel approach to the study of predator-prey interactions, and demonstrates that explicitly considering and linking bioenergetics, population demography and body mass phenotypes can provide novel insights into the mechanisms behind complex ecosystem processes

Resource selection by New York City deer reveals the effective interface between wildlife, zoonotic hazards and humans

Although the role of host movement in shaping infectious disease dynamics is widely acknowledged, methodological separation between animal movement and disease ecology has prevented researchers from leveraging empirical insights from movement data to advance landscape-scale understanding of infectious disease risk. To address this knowledge gap, we examine how movement behavior and resource utilization by white-tailed deer (Odocoileus virginianus) determine the distribution of blacklegged ticks (Ixodes scapularis) which depend on this host for dispersal in a highly fragmented New York City borough. Multi-scale hierarchical resource selection analysis and movement modeling provide insight into how deer’s individual movements construct the risk landscape for human exposure to the Lyme disease zoonotic hazard – infected I. scapularis. We conclude the distribution of tick-borne disease risk is the result of individual resource selection by deer across spatial scales in response to habitat fragmentation and anthropogenic disturbances

Quantifying the errors in animal contacts recorded by proximity loggers

Automated contact detection by means of proximity loggers permits the measurement of encounters between individuals (animal-animal contacts) and the time spent by individuals in the proximity of a focal resource of interest (animal-fixed logger contacts). The ecological inference derived from contact detection is intrinsically associated with the distance at which the contact occurred. But no proximity loggers currently exist that record this distance and therefore all distance estimations are associated with error. Here we applied a probabilistic approach to model the relationship between contact detection and inter-logger distance, and quantify the associated error, on free-ranging animals in semi-controlled settings. The probability of recording a contact declined with the distance between loggers, and this decline was steeper for weaker radio transmission powers. Even when proximity loggers were adjacent, contact detection was not guaranteed, irrespective of the radio transmission power. Accordingly, the precision and sensitivity of the system varied as a function of inter-logger distance, radio transmission power, and experimental setting (e.g., depending on animal body mass and fine-scale movements). By accounting for these relationships, we were able to estimate the probability that a detected contact occurred at a certain distance, and the probability that contacts were missed (i.e., false negatives). These calibration exercises have the potential to improve the predictability of the study and enhance the applicability of proximity loggers to key wildlife management issues such as disease transmission rates or wildlife use of landscape features and resources

Access to human-mobility data is essential for building a sustainable future

Funding: This article is a contribution of the COVID-19 Bio-Logging Initiative, which is funded in part by the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society (NGS-82315R-20) (both grants to C.R.) and endorsed by the United Nations Decade of Ocean Science for Sustainable Development. The authors also gratefully acknowledge support from the Kuni Endowed Junior Faculty Fellowship at the Bren School of Environmental Science & Management (to R.Y.O.); NASA FINESST (80NSSC22K1535) and the Yale Institute for Biospheric Studies (to D.E.S.); the National Biodiversity Future Center via the PNRR funds (Mission 4, Component 2, Investment 1.4) of the Italian Ministry of University and Resarch, Project CN00000033 (to F.C.); and the Natural Sciences and Engineering Research Council of Canada (to J.L.).Mobile devices, and other tracking technologies, generate detailed data on the movements and behavior of billions of people worldwide. At present, these data are predominantly used to pursue corporate interests. We argue that improving access to human-mobility data is essential for addressing urgent conservation and sustainability goals. Close collaboration between industry and the research community has the potential to generate substantive environmental and societal benefits.Peer reviewe

How Environmental Factors Impact Outdoor Wireless Sensor Networks: A Case Study

How do the characteristics of the surrounding en- vironment affect the ability of the nodes of a wireless sensor network (WSN) to communicate? Partial answers to this question can be found in the literature, but always with a focus on the short-term, small-scale behavior of individual links, as this directly informs the design of WSN protocols. In this paper, we are instead concerned with the large- scale behavior of the overall network, observed over a longer time scale, as our primary interest is to support the deployment of WSNs by characterizing the impact of the target environment. Motivated by a real-world wildlife monitoring application, we report about experimental campaigns in three outdoor environments characterized by varying degrees of vegetation. Experiments are repeated in summer and winter, to account for seasonal variations, and span multiple days, allowing us to assess variations induced by the succession of day and night. Our experiments focus primarily on characterizing the impact of the environment on the physical layer, but we also investigate how this is mirrored at higher layers. We analyze the experimental data along multiple dimensions, yielding quantitative answers to the aforementioned question, and eliciting trends and findings previously not reported in the literature. We argue that this type of study may inspire new methods to better estimate the performance of a WSN in its target deployment environment