Biology subjects

Morera-Pujol, V.

Publications and source records attributed to Morera-Pujol, V..

5 recordsLinked to original sources

bioRxiv2023

Age-related spatial ecology of Audouin's gull during the non-breeding season

Relationships between individuals age and the movement ecology and habitat preference of long-lived migratory birds still remain understudied. According to the exploration-refinement hypothesis it is thought that adults would select better and more productive areas for foraging than inexperienced juvenile birds would do. Additionally, age-related differences in migratory patterns and exploited habitats could be explained by the attempt to avoid competition between juveniles and adults. Here, we explored the differences in the migratory patterns, habitat selection and foraging behaviour between juvenile and adult Audouins gulls (Ichthyaetus audouinii), a species listed as vulnerable by the IUCN. We captured 9 juveniles and 8 adults in the colony of San Pedro (SE Spain) and equipped them with high-resolution 5-min programmed GPS to track their postnuptial/first migration and non-breeding destinations. First, juveniles tended to migrate longer distances than adults did. Second, the time spent foraging between age groups did not differ. Third, freshwater masses constituted an essential habitat during the non-breeding season for both juveniles and adults. Fourth, we found that adults used a greater variety of habitats than juveniles did, but adults positively select foraging habitats despite the low availability while juveniles do not. Finally, repeatability in habitat use of individuals of the same age was rather low. We provided evidence of age-related differences in migratory patterns and habitat exploitation during the non-breeding period in a migratory seabird which can be explained by the avoidance of competition between adults and juveniles and the greater experience in foraging performance that adults have in comparison with juveniles.

ecology↗

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

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

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↗