Biology subjects

Ciuti, S.

Publications and source records attributed to Ciuti, S..

10 recordsLinked to original sources

bioRxiv2023

Does fear of humans predict anti-predator strategies in an ungulate hider species during fawning?

Humans are a major evolutionary force on wildlife via artificial selection. While often explored through the lens of extractive interactions (e.g., hunting) able to favour certain behavioural traits over others, the implications of non-extractive ones, such as wildlife feeding, remain under-studied. Research has recently shown that people tend to feed (and sometimes favour) a limited subset of bolder individuals within natural populations, although its dynamics and consequences are not fully clear. Using fallow deer living in a peri-urban setting as a model population, we studied whether mother deer that display reduced fear of humans and consistently approach them for food adopt weaker anti-predator strategies by selecting for fawning bedsites that are less concealed and closer to human hotspots, allowing them to take advantage of additional artificial feeding opportunities in comparison to shier mothers in this population. Our dataset encompassed 171 fawns from 109 mothers across 4 years. Contrary to our expectations, we found that mothers that regularly accepted food from humans selected for more concealed bedsites farther away from them, giving their offspring better protection while also taking advantage of additional artificial food during lactating. Our results show marked behavioural adaptation by a subset of females, making this the first time that the link between tendency to approach humans and strategies to protect offspring is explored. Given previous findings that these begging females also deliver heavier fawns at birth, our research adds a piece to the complex puzzle describing human manipulation of behaviour in natural populations and its fitness consequences.

animal behavior and cognition↗

bioRxiv2023

Predicting forest damage using relative abundance of multiple deer species and national forest inventory data.

Human modification of landscape and natural resources have facilitated deer population irruptions across the world resulting in widespread human-wildlife conflicts. These conflicts occur across the field of natural resource management and negatively affect both the public and vested stakeholders when their livelihoods are placed at risk, for instance, the forestry sector. Deer, both native and non-native, at high densities can damage forest ecosystems impacting biodiversity and ecological functioning at multiple levels and can inflict large ecological and economic costs. The ecological drivers of forest damage and the roles of single and multiple co-occurring deer species is not well understood due to a lack of coordinated high resolution deer distribution, deer abundance and forest damage data. Here, we aim to disentangle the relationship between forest damage, forest characteristics and the roles deer play in damaging forest ecosystems. To achieve this, we adopt a novel approach integrating recent high resolution deer distribution data for multiple deer species (native and non-native) and combining them with forest inventory data collected in 1,681 sampling stations across Ireland to provide risk scenario predictions for practitioners to use on a national scale. Forest characteristics played a key role in the severity and type of damage risk that deer posed. We found all damage types were more prevalent in forests with greater tree densities where deer are more likely to find refuge from human disturbance. Bark stripping damage was more prevalent in mature forests with high tree diversity and ground level flora (e.g., bryophytes, herbs, and shrubs). Similarly, browsing damage was more prevalent in forests with greater tree richness but with understorey vegetation dominated by grass and ferns. Fraying damage was more common in mixed woodlands with understory dominated by bryophytes and grass. Crucially, we found that type and severity of forest damage were shaped by the interaction of multiple deer species occurring simultaneously, particularly at high densities, suggesting subtle inter-species competition and exclusion/partition dynamics that require further investigation to understand the ecological mechanism. Finally, we produce risk scenarios of forest damage by co-occurring deer species and precisely predict where damage is likely to occur on a national scale. We predict high levels of damage in sika and/or red deer hotspots, matching areas of highly concentrated deer distributions. This study highlights the ecological drivers and the role that co-occurring native and non-native deer species have on forest damage within a large spatial scale. By combining reliable species distribution models with the national forest inventory data, we can now provide a useful tool for practitioners to help alleviate and mitigate forest damage and human wildlife conflicts.

ecology↗

bioRxiv2023

Fawn bedsite selection by a large ungulate living in a peri-urban area

Human-wildlife conflict in expanding peri-urban and urban areas is of increasing concern, as a result of growing human populations along with the associated anthropogenic footprint on wildlife habitats. Empirical data from wildlife research carried out within human dominated landscapes are key to understanding the effects of human pressures on wildlife ecology and behaviour, exploring wildlife behavioural flexibility (or phenotypic plasticity), and informing wildlife management decisions. Here, we explored how female fallow deer (Dama dama) responded to human and dog presence during the birthing period in the largest walled urban park in Europe. We collected data on 477 bedsites utilised by 283 neonate fawns across three consecutive fawning seasons, gathered fine-scale data on humans and dogs space use, and built Resource Selection Functions at multiple spatial scales. We found that, when choosing bedsites to give birth and leave fawns unattended, fallow deer mothers significantly avoided hotspots of park visitors on foot (and their dogs) along the hiking trail routes. Bedsites were also unlikely to be in close proximity of paved roads used by vehicle traffic. Additionally, fallow deer mothers were found to select for dense understory vegetation for bedsites, providing low visibility to conceal their offspring. Our results provide detailed insights into bedsite spatial and habitat selection by a large herbivore in response to human activities, and we provide clear indications to wildlife managers to preserve established fawning sites and alleviate human-wildlife conflict during a critical period of the deer annual biological cycle.

animal behavior and cognition↗

bioRxiv2023

First Eurasian cases of SARS-CoV-2 seropositivity in a free-ranging urban population of wild fallow deer

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infects wildlife. Recent studies highlighted that variants of concern (VOC) may expand into novel animal reservoirs with the potential for reverse zoonosis. North American white-tailed deer are the only deer species in which SARS-CoV-2 has been documented, raising the question whether further reservoir species exist as new VOC emerge. Here, we report the first cases of deer SARS-CoV-2 seropositivity in Eurasia, in a city population of fallow deer in Dublin, Ireland. Deer were seronegative in 2020 (circulating variant in humans: Alpha), one animal was seropositive in 2021 (Delta variant), and 57% of animals tested in 2022 were seropositive (Omicron variant). Ex vivo, a clinical isolate of Omicron BA.1 infected fallow deer precision cut lung slice type-2 pneumocytes, also a major target of infection in human lungs. Our findings suggest a change in host tropism as new variants emerged in the human reservoir, highlighting the importance of continued wildlife disease monitoring and limiting human-wildlife contacts. Teaser: This study is the first report of SARS-CoV-2 seropositivity in fallow deer, highlighting expansion of viral variants into new host reservoirs.

microbiology↗

bioRxiv2023

Curbing zoonotic disease spread in multi-host-species systems will require integrating novel data streams and analytical approaches: evidence from a scoping review of bovine tuberculosis

BackgroundZoonotic diseases represent a significant societal challenge in terms of their health and economic impacts. One Health approaches to managing zoonotic diseases are becoming more prevalent, but require novel thinking, tools and cross-disciplinary collaboration. Bovine tuberculosis (bTB) is one example of a costly One Health challenge with a complex epidemiology involving human, domestic animal, wildlife and environmental factors, which require sophisticated collaborative approaches. ObjectiveWe undertook a scoping review of multi-host bTB epidemiology to identify recent trends in species publication focus, methodologies, scales and One Health approaches. We aimed to identify research gaps where novel research could provide insights to inform control policy, for bTB and other zoonoses. ResultsThe review included 167 articles. We found different levels of research attention across episystems, with a significant proportion of the literature focusing on the badger-cattle-TB episystem, with far less attention given to the multi-host episystems of southern Africa. We found a limited number of studies focusing on management solutions and their efficacy, with very few studies looking at modelling exit strategies. Surprisingly, only a small number of studies looked at the effect of human disturbances on the spread of bTB involving wildlife hosts. Most of the studies we reviewed focused on the effect of badger vaccination and culling on bTB dynamics with few looking at how roads, human perturbations and habitat change may affect wildlife movement and disease spread. Finally, we observed a lack of studies considering the effect of weather variables on bTB spread, which is particularly relevant when studying zoonoses under climate change scenarios. ConclusionsSignificant technological and methodological advances have been applied to bTB episystems, providing explicit insights into its spread and maintenance across populations. We identified a prominent bias towards certain species and locations. Generating more high-quality empirical data on wildlife host distribution and abundance, high-resolution individual behaviours and greater use of mathematical models and simulations are key areas for future research. Integrating data sources across disciplines, and a "virtuous cycle" of well-designed empirical data collection linked with mathematical and simulation modelling could provide additional gains for policy-makers and managers, enabling optimised bTB management with broader insights for other zoonoses.

ecology↗

bioRxiv2023

Sex-specific relationship between maternal and neonate cortisol in a free-ranging large mammal

Maternal phenotypes can have long-term effects on offspring phenotypes. These maternal effects may begin during gestation, when maternal glucocorticoid (GC) levels may affect foetal GC levels, thereby having an organizational effect on the offspring phenotype. Recent studies have showed that maternal effects may be different between the sexes. However, how maternal GC levels relate to foetal levels is still not completely understood. Here we related, for the first time in a free-ranging large mammal, the fallow deer (Dama dama), maternal GC levels with foetal in utero GC levels. We did this in a non-invasive way by quantifying cortisol metabolites from faecal samples collected from pregnant does during late gestation, as proxy for maternal GC level. These were then related to GC levels from hair of their neonate offspring (n = 40). We have shown that maternal GC levels were positively associated with foetal GC levels, but only in female offspring. These findings highlight sex differences, which may have evolved to optimize male growth at the cost of survival.

ecology↗

bioRxiv2023

Assessing bias and robustness of social network metrics using GPS based radio-telemetry data

O_LISocial network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, dynamical processes, and transmission events such as the spread of disease. However, the accuracy of estimated social network metrics depends on the proportion of individuals sampled, actual sample size, and frequency of observations. Robustness of network metrics derived from a sample has thus far been examined through various simulation studies. However, simulated data do not necessarily reflect the nuances of real empirical data. C_LIO_LIWe used some of the largest available GPS telemetry relocation datasets from five species of ungulates characterised by different behavioural and ecological traits and living in distinct environmental contexts to study the bias and robustness of social network metrics. We introduced novel statistical methods to quantify the uncertainty in network metrics obtained from a partial population suited to autocorrelated data such as telemetry relocations. We analysed how social network metrics respond to down-sampling from the observed data and applied pre-network data permutation techniques, a bootstrapping approach, correlation, and regression analyses to assess the stability of network metrics when based on samples of a population. C_LIO_LIWe found that global network metrics like density remain robust when the sample size is lowered, whereas some local network metrics, such as eigenvector centrality, are entirely unreliable when a large proportion of the population is not monitored. We show how to construct confidence intervals around the point estimates of these metrics representing the uncertainty as a function of the number of nodes in the network. C_LIO_LIOur uncertainty estimates enable the statistical comparison of social network metrics under different conditions, such as analysing daily and seasonal changes in the density of a network. Despite the striking differences in the ecology and sociality among the five different ungulate species, the various social network metrics behave similarly under downsampling, suggesting that our approach can be applied to a wider range of species across vertebrates. Our methods can guide methodological decisions about animal social network research (e.g., sampling design and sample sizes) and allow more accurate ecological inferences from the available data. C_LI

animal behavior and cognition↗

bioRxiv2023

Bayesian areal disaggregation regression to predict wildlife distribution and relative density with low-resolution data

For species of conservation concern and human-wildlife conflict, it is imperative that spatial population data are available to design adaptive-management strategies and be prepared to meet challenges such as land use and climate change, disease outbreaks, and invasive species spread. This can be difficult, perhaps impossible, if spatially explicit wildlife data are not available. Low-resolution areal counts, however, are common in wildlife monitoring, i.e., number of animals reported for a region, usually corresponding to administrative subdivisions, e.g., region, province, county, departments, or cantons. Bayesian areal disaggregation regression is a solution to exploit areal counts and provide conservation biologists with high-resolution species distribution predictive models. This method originated in epidemiology but lacks experimentation in ecology. It provides a plethora of applications to change the way we collect and analyse data for wildlife populations. Based on high-resolution environmental rasters, the disaggregation method disaggregates the number of individuals observed in a region and distributes them at the pixel level (e.g., 5x5 km or finer resolution), therefore converting the low-resolution data into high-resolution distribution and indices of relative density. In our demonstrative study, we disaggregated areal count data from hunting bag returns to disentangle the changing distribution and population dynamics of three deer species (red, sika and fallow) in Ireland from 2000 to 2018. We show an application of Bayesian areal disaggregation regression method and document marked increases in relative population density and extensive range expansion for each of the three deer species across Ireland. We challenged our disaggregated model predictions by correlating them with independent deer surveys carried out in field sites and alternative deer distribution models built using presence-only and presence-absence data. Finding high correlation with both independent datasets, we highlighted the accurate ability of Bayesian areal disaggregation regression to capture fine scale spatial patterns of animal distribution. This study opens new scenarios for wildlife managers and conservation biologists to reliably use regional count data disregarded so far in species distribution modelling. Thus, representing a step forward in our ability to monitor wildlife population and meet challenges in our changing world. Open data statementData used in the study has been publicly archived for reproducibility. Data archive DOI: 10.6084/m9.figshare.21890505

ecology↗

bioRxiv2022

Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance.

The use of georeferenced information on the presence of a species to predict its distribution across a geographic area is one of the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have more abundant low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually less abundant and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive and abundant presence-only data. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. These models, however, have only been applied to simulated data so far. Here, for the first time, we apply this approach to empirical data, using presence-absence and presence-only data for the three main deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km2 resolution across the island. Models predictions were associated to spatial estimate of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we validated the three species-specific models using independent deer hunting returns. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unused and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.

ecology↗

bioRxiv2022

Neonate personality affects early-life resource acquisition in a large social mammal

Current debate in the field of animal personality revolves around whether personality is reflecting individual differences in resource allocation or acquisition. Despite the large body of literature, the proximate relationships between personality, resource allocation, and acquisition are still unclear, especially during early stages of development. Here we studied how among-individual differences in behaviour develop over the first 6 months of life, and their potential association with resource acquisition in a free-ranging population of fallow deer (Dama dama). We related proxies of neonate personality - i.e. neonate physiological (heart rate) and behavioural (latency to leave at release) anti-predator responses to human handling - to the proportion of time fawns allocated to scanning during their first summer and autumn of life. We then investigated whether there was a trade-off between scanning time and foraging time in these juveniles, and how it developed over their first 6 months of life. We found that neonates with longer latencies at capture (i.e. risk-takers) allocated less time scanning their environment, but that this relationship was only present when fawns were 3-6 months old during autumn, but not when fawns were only 1-2 months old during summer. We also found that time spent scanning was negatively related to time spent foraging - a relationship rarely tested in juveniles of large mammals - and that this relationship becomes stronger over time, as fawns gradually switch from a nutrition rich (milk) to a nutrition poor (grass) diet. Our results highlight a potential mechanistic pathway in which neonate personality may drive differences in early-life resource acquisition, through allocation, of a large social mammal.

ecology↗