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opencc-zeroJul 2020View details →

Data from: Environmental context determines the limiting demographic processes for plant recruitment across a species' elevational range

Plant recruitment is a multi-stage process determining population dynamics and species distributions. Still, we have limited understanding of how the successive demographic processes depend on the environmental context across species' distributional ranges. We conducted a large-scale transplant experiment to study recruitment of Pinus cembra over six years. We quantified the effects of environmental conditions on four demographic processes and identified the most limiting across and beyond the pines' elevational range over several years. Realized transition probabilities of the demographic processes varied substantially across the species' distributional range. Seed deposition decreased from the lower to the upper elevational range margin by 90 %, but this reduction was offset by increased seed germination and seedling survival. Dispersal limitation at the upper range margin potentially stems from unsuitable seed caching conditions for the animal seed disperser, whereas increased seed germination might result from enemy escape from fungal pathogens and favourable abiotic conditions at the upper range margin. Our multi-year experiment demonstrates that environmental context is decisive for the local relevance of particular demographic processes. We conclude that experimental studies identifying the limiting demographic processes controlling species distributions are key for projecting future range dynamics of plants.

opencc-zeroOct 2020View details →

Data from: Disentangling direct and indirect determinants of the duration of maternal care in brown bears: environmental context matters

The duration of maternal care, an important life history trait affecting population dynamics, varies greatly within species. Yet, our understanding of its predictors is limited, mostly correlative, and subject to misinterpretations, due to difficulties to disentangle the role of maternal- and offspring-related characteristics. We conducted path analysis on a dataset including 217 brown bear litters captured over a 29-year period in two populations in Sweden ("North" and "South") facing contrasting environmental conditions to identify and quantify the causes of variation in the duration of maternal care (1.5 years or 5 years).

opencc-zeroDec 2011View details →

Data from: The geographical and environmental determinants of genetic diversity for four alpine conifers of the European Alps

Climate is one of the most important drivers of local adaptation in forest tree species. Standing levels of genetic diversity and structure within and among natural populations of forest trees are determined by the interplay between climatic heterogeneity and the balance between selection and gene flow. To investigate this interplay single nucleotide polymorphisms (SNPs) were genotyped in 24 to 37 populations from four subalpine conifers, Abies alba Mill., Larix decidua L., Pinus cembra L. and Pinus mugo Turra, across their natural ranges in the Italian Alps and Apennines. Patterns of population structure were apparent using a Bayesian clustering program, STRUCTURE, which identified three to five genetic groups per species. Geographical correlates to these patterns, however, were only apparent for P. cembra. Multivariate environmental variables (i.e. principal components) were subsequently tested for association with SNPs using a Bayesian generalized linear mixed model. The majority of the SNPs, ranging from six in L. decidua to 18 in P. mugo, were associated with PC1, corresponding to winter precipitation and seasonal minimum temperature. In A. alba, four SNPs were associated with PC2, corresponding to the seasonal minimum temperature. Functional annotation of those genes with the orthologs in Arabidopsis revealed several genes involved in abiotic stress response. This study provides a detailed assessment of population structure and its association to environment and geography in four coniferous species in the Italian mountains.

opencc-zeroDec 2017View details →

Data from: Weather conditions determine attenuation and speed of sound: environmental limitations for monitoring and analysing bat echolocation

Echolocating bats are regularly studied to investigate auditory-guided behaviours and as important bioindicators. Bioacoustic monitoring methods based on echolocation calls are increasingly used for risk assessment and to ultimately inform conservation strategies for bats. As echolocation calls transmit through the air at the speed of sound, they undergo changes due to atmospheric and geometric attenuation. Both the speed of sound and atmospheric attenuation, however, are variable and determined by weather conditions, particularly temperature and relative humidity. Changing weather conditions thus cause variation in analysed call parameters, limiting our ability to detect and correctly analyse bat calls. Here, I use real-world weather data to exemplify the effect of varying weather conditions on the acoustic properties of air. I then present atmospheric attenuation and speed of sound for the global range of weather conditions and bat call frequencies to show their relative effects. Atmospheric attenuation is a non-linear function of call frequency, temperature, relative humidity and atmospheric pressure. While atmospheric attenuation is strongly positively correlated with call frequency, it is also significantly influenced by temperature and relative humidity in a complex non-linear fashion. Variable weather conditions thus result in variable and unknown effects on the recorded call, affecting estimates of call frequency and intensity, particularly for high frequencies. Weather-induced variation in speed of sound reaches up to about ±3%, but is generally much smaller and only relevant for acoustic localisation methods of bats. The frequency- and weather-dependent variation in atmospheric attenuation has a three-fold effect on bioacoustic monitoring of bats: it limits our capability (1) to monitor bats equally across time, space, and species, (2) to correctly measure frequency parameters of bat echolocation calls, particularly for high-frequencies, and (3) to correctly identify bat species in species-rich assemblies or for sympatric species with similar call designs.

openJan 2022View details →

Dataset used in the assessment of the article "Basic urban services fail to neutralise environmental determinants of 'rattiness', a composite metric of rat abundance"

This data was collected in four Brazilian informal settlements located in the city of Salvador - the third largest city of Brazil - in the wet season of the year 2018. The data includes the collection of three rat abundance metrics - namely, rat marks on track plates, rats caught in live traps and removed, and presence of rat signs (faecal droppings, trails and active burrows) - in a total of 529 independent sampling locations. In addition to the single rat metrics, which were also used to compose the rattiness outcome, the data contain the baseline environmental and socioeconomic (surveyed and mapped) variables and the BUS variables considered in Carvalho-Pereira et al., 2023. (the data will be available soon)

opencc-by-4.0Jun 2024View details →

Identification of Environmental Determinants Involved in the Distribution of Burkholderia pseudomallei in Southeast Asia using MaxEnt software

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →

Supplementary data: model results for "Labor market evolution is a key determinant of global agroeconomic and environmental futures"

This compressed dataset includes the queried CVS files from 16 GCAM data bases generated for the study titled "Labor market evolution is a key determinant of global agroeconomic and environmental futures". The data sets provided here came from the GCAM model output. Please find the model and code information at the GitHub repo: <a href="https://github.com/realxinzhao/paper-nc2024-LandBasedCDR-GCAM" target="_blank" rel="noopener">realxinzhao/paper-nc2024-LandBasedCDR-GCAM</a>. In addition, the data were used for generating results used in the paper. See more information at <a href="https://github.com/realxinzhao/paper-nfood2024-AgLaborEvolution-DisplayItems" target="_blank" rel="noopener">realxinzhao/paper-nfood2024-AgLaborEvolution-DisplayItems</a>.

opencc-zeroDec 2017View details →

Data from: What determines prey selection in owls? Roles of prey traits, prey class, environmental variables, and taxonomic specialization

Ecological theory suggests that prey size should increase with predator size, but this trend may be masked by other factors affecting prey selection, such as environmental constraints or specific prey preferences of predator species. Owls are an ideal case study for exploring how predator body size affects prey selection in the presence of other factors due to the ease of analyzing their diets from owl pellets and their widespread distributions, allowing interspecific comparisons between variable habitats. Here, we analyze various dimensions of prey resource selection among owls, including prey size, taxonomy (i.e., whether or not particular taxa are favored regardless of their size), and prey traits (movement type, social structure, activity pattern, and diet). We collected pellets of five sympatric owl species (Athene noctua, Tyto alba, Asio otus, Strix aluco, and Bubo bubo) from 78 sites across the Mediterranean Levant. Prey intake was compared between sites, with various environmental variables and owl species as predictors of abundance. Despite significant environmental impacts on prey intake, some key patterns emerge among owl species studied. Owls select prey by predator body size: Larger owls tend to feed on wider ranges of prey sizes, leading to higher means. In addition, guild members show both specialization and generalism in terms of prey taxa, sometimes in contrast with the expectations of the predator–prey body size hypothesis. Our results suggest that while predator body size is an important factor in prey selection, taxon specialization by predator species also has considerable impact.

opencc-zeroDec 2017View details →

Data from: Demographic, environmental and genetic determinants of mating success in captive koalas (Phascolarctos cinereus)

Many factors have been shown to affect mating behavior. For instance, genes of the major histocompatibility complex (MHC) are known to influence mate choice in a wide variety of vertebrate species. The genetic management of captive populations can be confounded if intrinsic mate choice reduces or eliminates reproductive success between carefully chosen breeding pairs. For example, the San Diego Zoo koala colony only has a 45% copulation rate for matched individuals. Herein, we investigated determinants of koala mating success using breeding records (1984-2010) and genotypes for 52 individuals at four MHC markers. We quantified MHC diversity according to functional amino acids, heterozygosity, and the probability of producing a heterozygous offspring. We then used categorical analysis and logistic regression to investigate both copulation and parturition success. In addition, we also examined age, day length, and average pairwise kinship. Our post-hoc power analysis indicates that at a power level of 1 – β = 0.8, we should have been able to detect strong MHC preferences. However, we did not find a significant MHC effect on either copulation or parturition success with one exception: pairs with lower or no production of a joey had significantly lower MHC functional amino acid diversity in the categorical analysis. In contrast, day length and dam age (or age difference of the pair) consistently had an effect on mating success. These findings may be leveraged to improve the success of attempted pairs, conserve resources, and facilitate genetic management.

Fig. 4 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 4. Spatial variation in the partial coefficients of determination (R2) for three environmental hypotheses and their shared effects. The coefficients are: energy (E), heterogeneity (H), seasonality (S), shared contribution between energy and heterogeneity (E: H), energy and seasonality (E: S), heterogeneity and seasonality (H: S), and the contribution shared among energy, heterogeneity and seasonality (E: H: S).

Fig. 2 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 2. Distribution of coefficients of determination (R2) of GWR for the analysis of bat species richness regressed on the predictors of the three environmental hypotheses combined.

Fig. 1 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 1. Global pattern of bat species richness. Legend corresponds to the number of bat species per 2◦ × 2◦ grid cell. Letters represent the zoogeographic realms (Holt et al. 2013): Na = Nearctic, P = Panamanian, Nt = Neotropical, Pa = Palearctic, Sa = Saharo-Arabian, At = Afrotropical, M = Madagascan, Or = Oriental, Au = Australian, Sj = Sino-Japanese, Oc = Oceanian.

Fig. 3 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 3. Spatial non-stationarity in the effect (R2) of the predictors of the three environmental hypotheses combined to explain the global pattern of bat species richness.

Genetic & Environmental Determinants Of Immune Phenotype Variance: A Longitudinal Assessment

ClinicalTrials.gov study NCT05381857. IPD Sharing: Not stated. Countries: 1. Publications: 3.

controlledIPD-YESFeb 2026View details →

TEDDY - The Environmental Determinants of Diabetes in the Young

ClinicalTrials.gov study NCT00279318. IPD Sharing: YES. Countries: 4. Publications: 12.

HEBE Project - Healthy Aging Versus Inflamm-aging: the Role of Physical Exercise in Modulating the Biomarkers of Age-associated and Environmentally Determined Chronic Diseases

ClinicalTrials.gov study NCT05815732. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.

Environmental and Genetic Determinants of NPC

ClinicalTrials.gov study NCT01257100. IPD Sharing: Not stated. Countries: 1. Publications: 3.

Chemometers to Determine the Environmental and Human Exposome by Mixtures of Pollutants

ClinicalTrials.gov study NCT05927077. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.

Genetic & Environmental Determinants Of Immune Phenotype Variance: Establishing A Path Towards Personalized Medicine

ClinicalTrials.gov study NCT01699893. IPD Sharing: Not stated. Countries: 1. Publications: 3.