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opencc-zeroAug 2023View details →

Data from: An adaptive biomolecular condensation response is conserved across environmentally divergent species

Cells must sense and respond to sudden maladaptive environmental changes—stresses—to survive and thrive. Across eukaryotes, stresses such as heat shock trigger conserved responses: growth arrest, a specific transcriptional response, and biomolecular condensation of protein and mRNA into structures known as stress granules under severe stress. The composition, formation mechanism, adaptive significance, and even evolutionary conservation of these condensed structures remain enigmatic. Here we provide an unprecedented view into stress-triggered condensation, its evolutionary conservation and tuning, and its integration into other well-studied aspects of the stress response. Using three morphologically near-identical budding yeast species adapted to different thermal environments and diverged by up to 100 million years, we show that proteome-scale biomolecular condensation is tuned to species-specific thermal niches, closely tracking corresponding growth and transcriptional responses. In each species, poly(A)-binding protein—a core marker of stress granules—condenses in isolation at species-specific temperatures, with conserved molecular features and conformational changes modulating condensation. From the ecological to the molecular scale, our results reveal previously unappreciated levels of evolutionary selection in the eukaryotic stress response, while establishing a rich, tractable system for further inquiry.

opencc-by-4.0Aug 2023View details →

Reduction in the Tropical High Cloud Fraction in Response to an Indirect Weakening of the Hadley Cell

NetCDF files for figures in "Reduction in the Tropical High Cloud Fraction in Response to an Indirect Weakening of the Hadley Cell" submitted to Journal of Advances in Modeling Earth Systems. Figure 1: Prescribed SST for control and perturbed simulations:  <ul> <li>sst_ctrl_annualzonalmeans.nc</li> <li>sst_pert_annualzonalmeans.nc</li> </ul> Figure 2: Mass meridional stream function data for control and perturbed simulations: Figure 2a: PC2 cloud scheme + Gregory-Rowntree mass-flux convection scheme <ul> <li>mmsf_pc2gr_ctrl_annualmeans.nc</li> <li>mmsf_pc2gr_pert_annualmeans.nc</li> </ul> Figure 2b: Smith cloud scheme + Gregory-Rowntree mass-flux convection scheme <ul> <li>mmsf_smithgr_ctrl_annualmeans.nc</li> <li>mmsf_smithgr_pert_annualmeans.nc</li> </ul> Figure 3a: Ice cloud fraction data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>Cf_pc2gr_ctrl_annualzonalmeans.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3b: Ice cloud fraction data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>Cf_smithgr_ctrl_annualzonalmeans.nc</li> <li>Cf_smithgr_pert_annualzonalmeans.nc</li> </ul> Figure 3c: Relative humidity data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>RH_pc2gr_ctrl_annualzonalmeans.nc</li> <li>RH_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3d: Relative humidity for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>RH_smithgr_ctrl_annualzonalmeans.nc</li> <li>RH_smithgr_pert_annualzonalmeans.nc</li> </ul> Figure 3e: Specific humidity data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>q_pc2gr_ctrl_annualzonalmeans.nc</li> <li>q_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3f: Specific humidity data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>q_smithgr_ctrl_annualzonalmeans.nc</li> <li>q_smithgr_pert_annualzonalmeans.nc</li> </ul> Figure 3g: Temperature data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>T_pc2gr_ctrl_annualzonalmeans.nc</li> <li>T_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3h: Temperature data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>T_smithgr_ctrl_annualzonalmeans.nc</li> <li>T_smithgr_pert_annualzonalmeans.nc</li> </ul>  Figure 5: Control and perturbed simulation data for ice cloud fraction and mass fraction of ice cloud condensates meaned over the tropical ascent region for PC2 scheme <ul> <li>Cf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>qcf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>qcf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 6a,b: Monthly-mean data of convective mass flux for control and perturbed simulations over the tropics between \(\pm\) 31 \(^\circ\)N for PC2 cloud scheme <ul> <li>convmassflux_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>convmassflux_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 6c,d: Monthly-mean data of net convective detrainment for control and perturbed simulations over the tropics between \(\pm\) 31 \(^\circ\)N for PC2 cloud scheme <ul> <li>netconvdetrain_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>netconvdetrain_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 6e,f: Monthly-mean data of tendency of ice cloud condensates due to convection for control and perturbed simulations over the tropics between \(\pm\) 31 \(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtconv_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>dqcfdtconv_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 7: Annual and zonal-mean data of net convective detrainment for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>netconvdetrain_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>netconvdetrain_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 8: Annual and zonal-mean data of net convective detrainment and ice cloud fraction for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>netconvdetrain_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>netconvdetrain_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 9: Annual and zonal-mean data of convective tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtconv_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqcfdtconv_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 10: Annual and zonal-mean data of net depositional tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtnetdep_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqcfdtnetdep_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 11: Monthly-mean data of net depositional tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropics between \(\pm\) 31\(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtnetdep_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>dqcfdtnetdep_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 12: Annual and zonal-mean data of specific humidity and advective tendency for specific humidity for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>q_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>q_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqdtadv_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqdtadv_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul> Figure 13: Control and perturbed simulation data for mean vertical advection of moisture, mean vertical moisture convergence, total advection of moisture, and total moisture convergence (meaned over the tropical ascent region for PC2 scheme for 15 simulation years) <ul> <li>MeanVerticalAdvectionOfMoisture_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalAdvectionOfMoisture_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalMoistureConvergence_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalMoistureConvergence_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureAdvection_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureAdvection_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureConvergence_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureConvergence_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> </ul>

opencc-by-4.0Aug 2023View details →

Molecular, biochemical and metabolomics analyses reveal constitutive and pathogen-induced defense responses of two sugarcane contrasting genotypes against leaf scald disease

Leaf scald caused by the bacteria Xanthomonas albilineans is one of the major concerns to sugarcane production. To breed for resistance, mechanisms underlying plant-pathogen interaction need deeper investigations. Herein, we evaluated sugarcane defense responses against X. albilineans using molecular and biochemical approaches to assess pathogen-triggered ROS, phytohormones and metabolomics in two contrasting sugarcane genotypes from 0.5-144 h post-inoculation (hpi). In addition, the infection process was monitored using TaqMan-based quantification of X. albilineans and the disease symptoms were evaluated in both genotypes after 15 d post-inoculation (dpi) The susceptible genotype presented a response to the infection at 0.5 hpi, accumulating defense-related metabolites such as phenolics and flavonoids with no significant defense responses thereafter, resulting in typical symptoms of leaf scald at 15 dpi. The resistant genotype did not respond to the infection at 0.5 hpi but constitutively presented higher levels of salicylic acid and of the same metabolites induced by the infection in the susceptible genotype. Moreover, two subsequent pathogen-induced metabolic responses at 12 and 144 hpi were observed only in the resistant genotype in terms of amino acids, quinic acids, coumarins, polyamines, flavonoids, phenolics and phenylpropanoids together with an increase of hydrogen peroxide, ROS-related genes expression, indole-3-acetic-acid and salicylic acid. Multilevel approaches revealed that constitutive chemical composition and metabolic reprogramming hampers the development of leaf scald at 48 and 72 hpi, reducing the disease symptoms in the resistant genotype at 15 dpi. Phenylpropanoid pathway is suggested as a strong candidate marker for breeding sugarcane resistant to leaf scald.

opencc-zeroAug 2023View details →

Differentially-expressed genes in blood in response to lipopolysaccharide in three rodent species

Infection tolerance in rodents was examined by injecting single-dose lipopolysaccharide (LPS) to induce inflammation in Peromyscus leucopus (LL stock), the white-footed deermouse also reservoir for Lyme disease and Mus musculus (outbred CD-1 breed), the house mouse, and Rattus norvegicus, the brown rat (Fischer strain). Reaction to LPS was analyzed in the blood of challenged rodents and compared to control animals. As natural reservoirs of zoonoses deermice show significant anti-inflammatory response as described in "An Infection-Tolerant Mammalian Reservoir for Several Zoonotic Agents Broadly Counters the Inflammatory Effects of Endotoxin" (<a href="https://doi.org/10.1128/mBio.00588-21)" rel="noopener">https://doi.org/10.1128/mBio.00588-21)</a>. The project and the description of the samples are described under the following NCBI BioProjects: PRJNA975149 (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA975149" rel="noopener">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA975149)</a> for mouse and deermouse and PRJNA973677 (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA973677)" rel="noopener">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA973677)</a>. This project is a follow-up project focusing on the transcriptomic analysis of the whole blood bulk RNA-seq and further analysis of differentially expressed genes (DEG) between the treatment arm and controls. Complete fold change and false discovery rate for all three rodent species used for the current Dryad set are previously published (<a href="https://doi.org/10.7280/D1470Z">https://doi.org/10.7280/D1470Z</a>). Here we report that deermice tolerance to infection is partly due to lower expression of interferon-gamma in comparison to mice and rats.

ASN Database - v3.2 - Database of Simulated Room Impulse Responses for Acoustic Sensor Networks Deployed in Complex Multi-Source Acoustic Environments

We present a large set of simulated room impulse responses for a multi-room apartment. The simulated apartment models a real vacation apartment for which a recorded set of audio data has already been made available in the context of the DCASE challenges. The impulse responses were rendered using a dense grid of sources and receivers by means of a hybrid auralization algorithm based on a low-order image-source method and deterministic cone tracing. The proposed data set can be used to generate a wide variety of acoustic scenes which, in turn, can benefit numerous data-demanding machine-learning algorithms. To obtain more information on the database, please visit <a href="https://github.com/Jearde/asn-database">the website</a>. Please read the license file (available in the GitHub repository) before using the database.

TAM survey questionnaire responses of a head-mounted assistive mouse controller for people with upper limb disability

This dataset contains TAM survey questionnaire and the corresponding responses of a head-mounted assistive mouse controller for people with upper limb disability.

opencc-by-4.0Jun 2023View details →

Corporate Social Responsibility & Students' Perceptions: Evidence from Indian Higher Education Institutions

Blank Instrument Coding File Data File Raw Data Table 1 - Methodology  Table 2 - Affirmations on CSR under study Table 3 - Characterization of the Sample Table 4 - Factor Analysis Results Table 5 - Validation of the assumptions of MANOVA

opencc-by-4.0Jun 2023View details →

Corporate Social Responsibility & Students' Perceptions: Evidence from Indian Higher Education Institutions

Raw Data Table 1 - Methodology  Table 2 - Affirmations on CSR under study Table 3 - Characterization of the Sample Table 4 - Factor Analysis Results Table 5 - Validation of the assumptions of MANOVA

opencc-zeroSep 2023View details →

Data for: Legacy community science data suggest reduced beached litter in response to a container deposit scheme at a local scale

Marine debris is causing significant environmental harm. Legislation is being implemented to reduce litter, including schemes like container deposit legislation that incentivize the return of commonly littered items for recycling. While there is a suggestion that these schemes reduce litter, no study has examined the long-term impact on the local environment before and after implementation. This study analyzes community science data from 8 years prior to the implementation of a container deposit scheme, paired with 3 years of data afterwards, to assess the scheme's effectiveness at a local scale. Although using legacy datasets limits the generalizability of the conclusions compared to dedicated studies, the findings strongly indicate that container deposit schemes effectively manage targeted containers but have little impact on overall waste abundances. Long-term datasets like these are invaluable for assessing the impact of management efforts.

opencc-by-4.0Dec 2022View details →

Brown and white layer pullet hybrids show different fear responses towards humans, but what role does light during incubation play in that?

Good early life conditions are increasingly recognized as essential to animal welfare later in life. The use of light during incubation might improve coping capacities and welfare in later life in poultry, by more closely approximating chicken natural environments compared to the current conventional incubation in darkness. Previous studies showed that lighted incubation resulted in more lateralized chicks, a more pronounced daily behavior rhythm, earlier onset of melatonin rhythms, and lower stress reactions to various stressors after hatching. Most existing research, however, has been conducted on broilers, and little information on lighted incubation is available for laying hens. In the current research, Dekalb White and ISA Brown eggs were incubated in complete darkness or in a cycle of green 12L:12D throughout incubation, and five fear of human tests were performed on the 387 chickens during the rearing phase. We expected dark-incubated chickens to show stronger fear responses than light-incubated chickens. That was only the case for one of 15 behavior measurements taken during the tests (p < 0.05). In addition, white layer hybrids are known to be flightier and more fearful than brown hybrids. In this study, white chickens indeed showed stronger fear responses than brown chickens in 12 of the 15 behavior measurements (p ≤ 0.002). Furthermore, we expected light during incubation to have stronger effects on white chickens than on brown chickens, because of the stronger transmission of light through white eggshells. However, the interaction between hybrid and incubation was never significant (p ≥ 0.18). Finally, contrary to our expectations, there was no effect of the incubation treatments or the hybrid on plasma corticosterone responses to a manual restraint test (p ≥ 0.36). Since there was a hybrid effect on behavior in this test, it is reasonable to think that behavior reflected coping style, rather than fear level. To conclude, the light regime used in this study does not seem as promising as expected to improve laying hen welfare. Finally, the brown hybrid was usually less fearful than the white hybrid, though there were some exceptions depending on the stressor, and that should be taken into account in research and in laying hen management.

Dataset: Frequency-Dependent Quadratic Response Properties and Two-photon Absorption from Relativistic Equation-of-Motion Coupled Cluster Theory

This dataset comprises outputs and post-processing results related to the paper "Frequency-Dependent Quadratic Response Properties and Two-photon Absorption from Relativistic Equation-of-Motion Coupled Cluster Theory", by Xiang Yuan, Loic Halbert, Lucas Visscher and Andre Severo Pereira Gomes.

Development and validation of a novel plasmid chassis system for screening of metabolite-responsive transcription factors

This dataset contains the raw data that lie at the basis of the results discussed in Chapter 3: Development and validation of a novel plasmid chassis system for screening of metabolite-responsive transcription factors of the PhD thesis of Amber Bernauw. The README.txt file provides more information on the different data files.

RESISTIRÉ Dataset: Narratives on inequalities caused by policy and societal responses to Covid-19 in Europe – second cycle

This dataset contains 295 narratives, including keywords and specifically telling quotes. It is the second of three sets of narratives collected and analysed in RESISTIRÉ. The narrative interviews were conducted in national languages by the consortium partners and a network of national researchers covering the EU27 (except Malta), and Iceland, Serbia, and the UK in December 2021-February 2022. Narrative interviewing is a qualitative research method that involves inviting participants to tell their own stories and experiences in their own words. The technique is used to collect and share a person’s story which entails both a research methodology and a mechanism for storytelling i.e. both a way of telling a story, and a way of knowing. Narratives as a technique can make visible how multiple sources of inequalities intersect, as well as the situational and contextual nature of inequalities from a single person’s perspective.

Data supporting: "Bayesian diagnosis of climate feedback evolution and forced temperature response"

This data set supports the paper: Calafat, F. M., & Cael, B. B. (2023). Bayesian diagnosis of climate feedback evolution and forced temperature response, Geophysical Research Letters, submitted. Please cite this paper when using this data set. Data description: <ul> <li>EBM_estimates.nc: this file contains Bayesian estimates from the energy balance model, including estimates of the climate feedback parameter and forced global average temperature change.</li> <li>EBM_input_data.nc: this file contains all of the data used as input to the Bayesian energy balance model.</li> </ul>

RESISTIRÉ Dataset: Narratives on inequalities caused by policy and societal responses to Covid-19 in Europe – third cycle

This dataset contains 289 narratives, including keywords and specifically telling quotes. It is the third of three sets of narratives collected and analysed in RESISTIRÉ. These narrative interviews were conducted in national languages by the consortium partners and a network of national researchers covering the EU27 (except Malta), and Iceland, Serbia, and the UK in September-December 2022.<a href="#_ftn1">[1]</a> Narrative interviewing is a qualitative research method that involves inviting participants to tell their own stories and experiences in their own words. The technique is used to collect and share a person’s story which entails both a research methodology and a mechanism for storytelling i.e. both a way of telling a story, and a way of knowing. Narratives as a technique can make visible how multiple sources of inequalities intersect, as well as the situational and contextual nature of inequalities from a single person’s perspective.   <a href="#_ftnref1">[1]</a> For an analysis of the narratives, see Sandström, L., Callerstig, A-C., Strid, S., Lionello, L., & Rossetti, F. (2023). RESISTIRE D4.3 Summary report on qualitative indicators - cycle 3. Zenodo. <a href="https://doi.org/10.5281/zenodo.7708724">https://doi.org/10.5281/zenodo.7708724</a>.

Comparison of heterologous β-alanine-responsive biosensors in Escherichia coli

This dataset contains the raw data that lie at the basis of the results discussed in Chapter 6: Comparison of heterologous β-alanine-responsive biosensors in Escherichia coli of the PhD thesis of Amber Bernauw. The README.txt file provides more information on the different data files.

opencc-by-4.0Jun 2022View details →

FUTURES land change forecasts in response to flooding in Charleston area, South Carolina (2019-2050)

Climate-aware scenarios of FUTURES land change projections in Charleston area (Cherleston, Dorchester, Berkeley) for 2019-2050. Zipped folder <code>results</code> contains FUTURES v3 simulation runs for 3 counties in South Carolina, USA (Charleston, Berkeley, Dorchester) from 2019 to 2050. Included are five climate-aware scenarios (reactive, managed retreat, resist, polarized population, trapped population) and one scenario which does not take future climate conditions into account (dynamic development). Each folder contains 50 monte-carlo simulation results with <code>developed_seed_X.tif</code> and <code>adapted_seed_X.tif</code> where <code>X</code> goes from 1 to 50. Values of <code>developed_seed_X.tif</code> range from -31 to 31, where the positive values represent simulation step when an undeveloped pixel was developed and the negative values represent simulation step when a developed pixel was abandoned. Zero stands for initial development. For example, value -11 means the particular pixel was abandoned in 2030. Note that values of raster files in <code>Dynamic development</code> folder range from -1 to 31, where -1 means undeveloped and the rest is the same as above. Values of <code>adapted_seed_X.tif</code> range from 0 to 100, where 0 means trapped (flooded but no adaptation), the other values represent return period for which the pixel is adapted (e.g. 2-, 5-, 10-, 20-, 50- and 100-year flood). The files' CRS is Albers Conic Equal Area, NAD83(2011) datum. Additionally, we include <code>migration_matrix.csv</code> derived from IRS data, that contains probability values of moving from an origin county in the case study (row) to any other counties (columns).

opencc-zeroSep 2023View details →

Numerical response of predator to prey: Dynamic interactions and population cycles in Eurasian lynx and roe deer

The dynamic interactions between predators and their prey have two fundamental processes; numerical and functional responses. Numerical response is defined as predator growth rate as a function of prey density or both prey and predator densities [dP/dt = f(N, P)]. Functional response is defined as the kill rate by an individual predator being a function of prey density or prey and predator densities combined. Although there are relatively many studies on the functional response in mammalian predators, numerical response remains poorly documented. We studied numerical response of Eurasian lynx (Lynx lynx) to various densities of its primary prey species, roe deer (Capreolus capreolus), and to itself (lynx). We exploited an unusual natural situation, spanning three decades where lynx, after a period of absence in central and southern Sweden, during which roe deer populations had grown to high densities, subsequently recolonized region after region, from north to south. We divided the study area into seven regions, with increasing productivity from north to south. We found strong effects of both roe deer density and lynx density on lynx numerical response. Thus, both resources and intraspecific competition for these resources are important to understand the lynx population dynamic. We built a series of deterministic lynx–roe deer models and applied them to the seven regions. We found a very good fit between these Lotka-Volterra-type models and the data. The deterministic models produced almost cyclic dynamics or dampened cycles in five of the seven regions. Thus, we documented population cycles in this large-predator-large-herbivore system, which is rarely done. The amplitudes in the dampened cycles decreased towards the south. Thus, the dynamics between lynx and roe deer became more stable with increasing carrying capacity for roe deer, which is related to higher productivity in the environment. This increased stability could be explained by variation in predation risk, where human presence can act as prey refugia, and by a more diverse prey guild that will weaken the direct interaction between lynx and roe deer.

opencc-zeroSep 2023View details →

Microhabitat conditions drive uncertainty of risk and shape neophobic responses in Trinidadian guppies, Poecilia reticulata

In response to uncertain risks, prey may rely on neophobic phenotypes to reduce the costs associated with the lack of information regarding local conditions. Neophobia has been shown to be driven by information reliability, ambient risk, and predator diversity, all of which shape uncertainty of risk. We similarly expect environmental conditions to shape uncertainty by interfering with information availability. In order to test how environmental variables might shape neophobic responses in Trinidadian guppies (Poecilia reticulata), we conducted an in situ field experiment of two high-predation risk guppy populations designed to determine how the "average" and "variance" of several environmental factors might influence the neophobic response to novel predator models and/or novel foraging patches. Our results suggest neophobia is shaped by water velocity, microhabitat complexity, pool width and depth, as well as substrate diversity and heterogeneity. Moreover, we found differential effects of the "average" and "variance" environmental variables on food- and predator-related neophobia. Our study highlights that assessment of neophobic drivers should consider predation risk, various microhabitat conditions, and neophobia being tested. Neophobic phenotypes are expected to increase the probability of prey survival and reproductive success (i.e. fitness), and are therefore likely linked to population health and species survival. Understanding the drivers and consequences of uncertainty of risk is an increasingly pressing issue, as ecological uncertainty increases with the combined effects of climate change, anthropogenic disturbances, and invasive species.

Dataset for the collected responses for the items measuring constructs affecting eHS non-acceptance behavior in Nigeria

This dataset is a collection of responses from the questionnaire distributed to study the e-health service non-acceptance behavior prominent in Nigeria. A total of 543 valid responses were collected. This research used an integration model based on TPB and SOR theory The dataset were analysed using PLS-SEM. Refer to the article for the results of this study. Note: CO = Communication overload; CHO = Choice overload; PR = Perceived oisk; HL = Health literacy; NA = Negative attitude; SN = Subjective norms; PBC = Perceived behavioral control; INTU = Intention not to use eHS; NAB = Non-acceptance behavior